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Dr. Guido Sciavicco
University of Ferrara

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0 Artificial Intelligence
0 Logic
0 Knowledge Representation and Reasoning
0 Evolutionary algorithm
0 Algorithm applications

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Artificial Intelligence
Evolutionary algorithm
Knowledge Representation and Reasoning

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Journal article
Published: 25 June 2021 in Information and Computation
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A Conditional Simple Temporal Network with Uncertainty and Decisions (CSTNUD) is a formalism for temporal plans that models controllable and uncontrollable durations as well as controllable and uncontrollable choices simultaneously. In the classic top-down model-based engineering approach, a designer builds CSTNUDs to model, validate and execute some temporal plans of interest. In this paper, we investigate a bottom-up approach by providing a deterministic polynomial time algorithm to mine a CSTNUD from a set of execution traces (i.e., a log). We provide a prototype implementation and we test it with a set of artificial data. Finally, we elaborate on consistency and controllability of mined networks.

ACS Style

Guido Sciavicco; Matteo Zavatteri; Tiziano Villa. Mining CSTNUDs Significant for a Set of Traces is Polynomial. Information and Computation 2021, 104773 .

AMA Style

Guido Sciavicco, Matteo Zavatteri, Tiziano Villa. Mining CSTNUDs Significant for a Set of Traces is Polynomial. Information and Computation. 2021; ():104773.

Chicago/Turabian Style

Guido Sciavicco; Matteo Zavatteri; Tiziano Villa. 2021. "Mining CSTNUDs Significant for a Set of Traces is Polynomial." Information and Computation , no. : 104773.

Journal article
Published: 10 May 2021 in Heliyon
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Geochemical fingerprinting is a rapidly expanding discipline in the earth and environmental sciences, anchored in the recognition that geological processes leave behind physical, chemical and sometimes also isotopic patterns in the samples. Furthermore, the geochemical fingerprinting of natural cycles (water, carbon, soil and biota fingerprinting) are influenced by the anthropogenic impact and by the climate change. So, their monitoring is a tool of resilience and adaptation. In recent years, computational statistics and artificial intelligence methods have started to be used to help the process of geochemical fingerprinting. In this paper we consider data from 57 wells located in the province of Ferrara (Italy), all belonging to the same geological group and separated into 4 different aquifers. The aquifer from which each well extracts its water is known only in 18 of the 57 cases, while in other 39 cases it can be only hypothesized based on geological considerations. We devise a novel technique for geochemical fingerprinting of groundwater by means of which we are able to identify the exact aquifer from which a sample is extracted with a sufficiently high accuracy. Then, we experimentally prove that out method is sensibly more accurate than typical statistical approaches, such as principal component analysis, for this particular problem.

ACS Style

A. Di Roma; E. Lucena-Sánchez; G. Sciavicco; C. Vaccaro. An intelligent clustering method for devising the geochemical fingerprint of underground aquifers. Heliyon 2021, 7, e07017 .

AMA Style

A. Di Roma, E. Lucena-Sánchez, G. Sciavicco, C. Vaccaro. An intelligent clustering method for devising the geochemical fingerprint of underground aquifers. Heliyon. 2021; 7 (5):e07017.

Chicago/Turabian Style

A. Di Roma; E. Lucena-Sánchez; G. Sciavicco; C. Vaccaro. 2021. "An intelligent clustering method for devising the geochemical fingerprint of underground aquifers." Heliyon 7, no. 5: e07017.

Journal article
Published: 20 April 2021 in Journal of Biomedical Informatics
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We designed, implemented, and tested a clinical decision support system at the Research Center for the Study of Menopause and Osteoporosis within the University of Ferrara (Italy). As an independent module of our system, we implemented an original machine learning system for rule extraction, enriched with a hierarchical extraction methodology and a novel rule evaluation technique. Such a module is used in everyday operation protocol, and it allows physicians to receive suggestions for prevention and treatment of osteoporosis. In this paper, we design and execute an experiment based on two years of data, in order to evaluate and report the reliability of our suggestion system. Our results are encouraging, and in some cases reach expected accuracies of around 90%.

ACS Style

G. Bonaccorsi; M. Giganti; M. Nitsenko; G. Pagliarini; G. Piva; G. Sciavicco. Predicting treatment recommendations in postmenopausal osteoporosis. Journal of Biomedical Informatics 2021, 118, 103780 .

AMA Style

G. Bonaccorsi, M. Giganti, M. Nitsenko, G. Pagliarini, G. Piva, G. Sciavicco. Predicting treatment recommendations in postmenopausal osteoporosis. Journal of Biomedical Informatics. 2021; 118 ():103780.

Chicago/Turabian Style

G. Bonaccorsi; M. Giganti; M. Nitsenko; G. Pagliarini; G. Piva; G. Sciavicco. 2021. "Predicting treatment recommendations in postmenopausal osteoporosis." Journal of Biomedical Informatics 118, no. : 103780.

Journal article
Published: 26 February 2021 in Algorithms
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Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.

ACS Style

Estrella Lucena-Sánchez; Guido Sciavicco; Ionel Stan. Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling. Algorithms 2021, 14, 76 .

AMA Style

Estrella Lucena-Sánchez, Guido Sciavicco, Ionel Stan. Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling. Algorithms. 2021; 14 (3):76.

Chicago/Turabian Style

Estrella Lucena-Sánchez; Guido Sciavicco; Ionel Stan. 2021. "Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling." Algorithms 14, no. 3: 76.

Journal article
Published: 30 November 2020 in Atmosphere
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Due to the unwavering interest of both residents and authorities in the air quality of urban agglomerations, we pose the following question in this paper: What impact do current and past meteorological factors and traffic flow intensity have on air quality? What is the impact of lagged variables on the fit of an explanation model, and how do they affect its ability to predict? We focused on NO2 and NOx concentrations, and conducted this research using hourly data from the city of Wrocław (western Poland) from 2015 to 2017; we used multi-objective optimization to determine the optimal delays. It turned out that for both NO2 and NOx, the past values for traffic flow, wind speed, and sunshine duration are more important than the current ones. We built random forest models on each of the pollutants for both the current and past values and discovered that including a lagged variable increases the resulting R2 from 0.51 to 0.56 for NO2 and from 0.46 to 0.52 for NOx. We also analyzed the feature importance in each model, and found that for NO2, a wind speed delay of more than three hours causes a significant decrease, while the importance of relative humidity increases with a seven-hour delay; likewise, wind speed increases the importance for NOx prediction with a two-hour delay. We concluded that, in pollutant concentration modeling, the possibility of a delayed effect of the independent variables should always be considered, because it can significantly increase the performance of the model and suggest unexpected relationships or dependencies.

ACS Style

Joanna Kamińska; Fernando Jiménez; Estrella Lucena-Sánchez; Guido Sciavicco; Tomasz Turek. Lag Variables in Nitrogen Oxide Concentration Modelling: A Case Study in Wrocław, Poland. Atmosphere 2020, 11, 1293 .

AMA Style

Joanna Kamińska, Fernando Jiménez, Estrella Lucena-Sánchez, Guido Sciavicco, Tomasz Turek. Lag Variables in Nitrogen Oxide Concentration Modelling: A Case Study in Wrocław, Poland. Atmosphere. 2020; 11 (12):1293.

Chicago/Turabian Style

Joanna Kamińska; Fernando Jiménez; Estrella Lucena-Sánchez; Guido Sciavicco; Tomasz Turek. 2020. "Lag Variables in Nitrogen Oxide Concentration Modelling: A Case Study in Wrocław, Poland." Atmosphere 11, no. 12: 1293.

Proceedings
Published: 01 January 2020 in Proceedings
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In order to refine the research on the impact of environmental factors on the concentration of pollutants in the air, in this paper, we present a mathematical model that allows the possibility of taking into account the past values of factors (explanatory variables) when modeling the current concentration of pollution. We conducted numerical analyzes based on hourly data from meteorological, traffic and air quality monitoring stations in Wrocław (Poland, Central Europe) from 2015–2017. In order to determine the optimal delay of each explanatory variable, we used a multi-objective optimization model (MO). It turned out that for the concentration of nitrogen oxides, delayed traffic flow, wind speed and sunshine duration time are more important than current ones. Then we built two random forest models: an actual model of current values of explanatory variables and a lag model with delayed variables determined by the MO method. Taking into account variables with an optimal delay (lag model) results in an increase in model accuracy for NO2 with R2 = 0.51 to 0.56 and for NOx from 0.46 to 0.52. We deduced that in pollutant concentrations modeling, the possibility of greater influence of variables with delay should always be considered because it can significantly increase the accuracy of the model and indicate additional relationships or dependencies.

ACS Style

Joanna A. Kamińska; Guido Sciavicco; Estrella Lucena-Sánchez; Fernando Jiménez. Lag Variables in Air Pollution Modeling Based on Traffic Flow and Meteorological Factors. Proceedings 2020, 51, 1 .

AMA Style

Joanna A. Kamińska, Guido Sciavicco, Estrella Lucena-Sánchez, Fernando Jiménez. Lag Variables in Air Pollution Modeling Based on Traffic Flow and Meteorological Factors. Proceedings. 2020; 51 (1):1.

Chicago/Turabian Style

Joanna A. Kamińska; Guido Sciavicco; Estrella Lucena-Sánchez; Fernando Jiménez. 2020. "Lag Variables in Air Pollution Modeling Based on Traffic Flow and Meteorological Factors." Proceedings 51, no. 1: 1.

Research article
Published: 01 January 2020 in Air, Soil and Water Research
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Anthropogenic environmental pollution is a known and indisputable issue, and the importance of searching for reliable mathematical models that help understanding the underlying process is witnessed by the extensive literature on the topic. In this article, we focus on the temporal aspects of the processes that govern the concentration of pollutants using typical explanatory variables, such as meteorological values and traffic flows. We develop a novel technique based on multiobjective optimization and linear regression to find optimal delays for each variable, and then we apply such delays to our data to evaluate the improvement that can be obtained with respect to learning an explanatory model with standard techniques. We found that optimizing delays can, in some cases, improve the accuracy of the final model up to 15%.

ACS Style

Joanna Kamińska; Estrella Lucena-Sánchez; Guido Sciavicco. Temporal Aspects in Air Quality Modeling—A Case Study in Wrocław. Air, Soil and Water Research 2020, 13, 1 .

AMA Style

Joanna Kamińska, Estrella Lucena-Sánchez, Guido Sciavicco. Temporal Aspects in Air Quality Modeling—A Case Study in Wrocław. Air, Soil and Water Research. 2020; 13 ():1.

Chicago/Turabian Style

Joanna Kamińska; Estrella Lucena-Sánchez; Guido Sciavicco. 2020. "Temporal Aspects in Air Quality Modeling—A Case Study in Wrocław." Air, Soil and Water Research 13, no. : 1.

Conference paper
Published: 10 December 2019 in EPiC Series in Computing
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Temporal dataset evaluation is the problem of establishing to what extent a set of temporal data (histories) complies with a given temporal condition. Checking interval temporal logic formulas against a finite model has been recently proposed, and proved successful, as a tool to solve such a problem. In this paper, we address the problem of checking interval temporal logic specifications, supporting interval length constraints, against infinite, finitely representable models, and we show the applicability of the resulting procedure to the evaluation of incomplete temporal datasets viewed as finite prefixes of ultimately-periodic histories.

ACS Style

Dario Della Monica; Angelo Montanari; Aniello Murano; Guido Sciavicco. Ultimately-periodic Interval Model Checking for Temporal Dataset Evaluation. EPiC Series in Computing 2019, 65, 28 -41.

AMA Style

Dario Della Monica, Angelo Montanari, Aniello Murano, Guido Sciavicco. Ultimately-periodic Interval Model Checking for Temporal Dataset Evaluation. EPiC Series in Computing. 2019; 65 ():28-41.

Chicago/Turabian Style

Dario Della Monica; Angelo Montanari; Aniello Murano; Guido Sciavicco. 2019. "Ultimately-periodic Interval Model Checking for Temporal Dataset Evaluation." EPiC Series in Computing 65, no. : 28-41.

Conference paper
Published: 01 September 2019 in Communications in Computer and Information Science
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The temporal aspects often play an important role in information extraction. Given the peculiarities of temporal data, their management typically requires the use of dedicated algorithms, that make the overall data mining process complex, especially in those cases in which a dataset is characterised by both temporal and atemporal information. In such a situation, typical solutions include combining different algorithms for the independent handling of the temporal and atemporal parts, or relying on an encoding of temporal data that makes it possible to apply classical machine learning algorithms (such as with the use of lagged variables). This work investigates the management of temporal information in an environmental problem, that is, assessing the relationships between concentrations of the pollutants \(NO_2\), \(NO_X\), and \(PM_{2.5}\), and a set of independent variables that include meteorological conditions and traffic flow in the city of Wrocław (Poland). We show that taking into account temporal information by means of lagged variables leads to better results with respect to atemporal models. More importantly, an even higher performance may be achieved by making use of a recently proposed decision tree model, called J48SS, that is capable of handling heterogeneous datasets consisting of static (i.e., categorical and numerical) attributes, as well as sequential and time series data. Such an outcome highlights the importance of proper temporal data modelling.

ACS Style

Andrea Brunello; Joanna Kamińska; Enrico Marzano; Angelo Montanari; Guido Sciavicco; Tomasz Turek. Assessing the Role of Temporal Information in Modelling Short-Term Air Pollution Effects Based on Traffic and Meteorological Conditions: A Case Study in Wrocław. Communications in Computer and Information Science 2019, 463 -474.

AMA Style

Andrea Brunello, Joanna Kamińska, Enrico Marzano, Angelo Montanari, Guido Sciavicco, Tomasz Turek. Assessing the Role of Temporal Information in Modelling Short-Term Air Pollution Effects Based on Traffic and Meteorological Conditions: A Case Study in Wrocław. Communications in Computer and Information Science. 2019; ():463-474.

Chicago/Turabian Style

Andrea Brunello; Joanna Kamińska; Enrico Marzano; Angelo Montanari; Guido Sciavicco; Tomasz Turek. 2019. "Assessing the Role of Temporal Information in Modelling Short-Term Air Pollution Effects Based on Traffic and Meteorological Conditions: A Case Study in Wrocław." Communications in Computer and Information Science , no. : 463-474.

Conference paper
Published: 10 May 2019 in Computer Vision
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Extracting rules from temporal series is a well-established temporal data mining technique. The current literature contains a number of different algorithms and experiments that allow one to abstract temporal series and, later, extract meaningful rules from them. In this paper, we approach this problem in a rather general way, without resorting, as many other methods, to expert knowledge and ad-hoc solutions. Our very simple temporal abstraction method allows us to transform time series into timelines, which can be then used for logical temporal rule extraction using an already existing temporal adaptation of the algorithm APRIORI. We have tested this approach on real data, obtaining promising results.

ACS Style

Guido Sciavicco; Ionel Eduard Stan; Alessandro Vaccari. Towards a General Method for Logical Rule Extraction from Time Series. Computer Vision 2019, 3 -12.

AMA Style

Guido Sciavicco, Ionel Eduard Stan, Alessandro Vaccari. Towards a General Method for Logical Rule Extraction from Time Series. Computer Vision. 2019; ():3-12.

Chicago/Turabian Style

Guido Sciavicco; Ionel Eduard Stan; Alessandro Vaccari. 2019. "Towards a General Method for Logical Rule Extraction from Time Series." Computer Vision , no. : 3-12.

Conference paper
Published: 06 May 2019 in Computer Vision
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Decision trees are simple, yet powerful, classification models used to classify categorical and numerical data, and, despite their simplicity, they are commonly used in operations research and management, as well as in knowledge mining. From a logical point of view, a decision tree can be seen as a structured set of logical rules written in propositional logic. Since knowledge mining is rapidly evolving towards temporal knowledge mining, and since in many cases temporal information is best described by interval temporal logics, propositional logic decision trees may evolve towards interval temporal logic decision trees. In this paper, we define the problem of interval temporal logic decision tree learning, and propose a solution that generalizes classical decision tree learning.

ACS Style

Andrea Brunello; Guido Sciavicco; Ionel Eduard Stan. Interval Temporal Logic Decision Tree Learning. Computer Vision 2019, 778 -793.

AMA Style

Andrea Brunello, Guido Sciavicco, Ionel Eduard Stan. Interval Temporal Logic Decision Tree Learning. Computer Vision. 2019; ():778-793.

Chicago/Turabian Style

Andrea Brunello; Guido Sciavicco; Ionel Eduard Stan. 2019. "Interval Temporal Logic Decision Tree Learning." Computer Vision , no. : 778-793.

Journal article
Published: 15 April 2019 in IEEE Transactions on Learning Technologies
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In the European academic systems, the public founding to single universities depends on many factors, which are periodically evaluated. One of such factors is the rate of success, that is, the rate of students that do complete their course of study. At many levels, therefore, there is an increasing interest in being able to predict the risk that a student will abandon the studies, so that (specific, personal) corrective actions may be designed. In this paper, we propose an innovative temporal optimization model that is able to identify the earliest moment in a student's career in which a reliable prediction can be made concerning his/her risk of dropping out from the course of studies. Unlike most available models, our solution can be based on the academic behaviour alone, and our evidence suggests that by ignoring classically used attributes such as the gender or the results of pre-academic studies one obtains more accurate, and less biased, models. We tested our system on real data from the three-years Degree in Computer Science offered by the University of Ferrara (Italy).

ACS Style

Fernando Jimenez; Alessia Paoletti; Gracia Sanchez; Guido Sciavicco. Predicting the Risk of Academic Dropout With Temporal Multi-Objective Optimization. IEEE Transactions on Learning Technologies 2019, 12, 225 -236.

AMA Style

Fernando Jimenez, Alessia Paoletti, Gracia Sanchez, Guido Sciavicco. Predicting the Risk of Academic Dropout With Temporal Multi-Objective Optimization. IEEE Transactions on Learning Technologies. 2019; 12 (2):225-236.

Chicago/Turabian Style

Fernando Jimenez; Alessia Paoletti; Gracia Sanchez; Guido Sciavicco. 2019. "Predicting the Risk of Academic Dropout With Temporal Multi-Objective Optimization." IEEE Transactions on Learning Technologies 12, no. 2: 225-236.

Original article
Published: 06 March 2019 in Expert Systems
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In this work, a data set describing phone interactions arising in a multichannel and multiskill contact centre is considered with the aim of classifying inbound sessions into those that will be eventually managed by an agent and those that, instead, will be abandoned before. More precisely, the goal of the work is to extract interpretable pieces of information that allow us to predict whether a user will or will not abandon a call, which may turn out to be very useful for the purpose of contact centre managing. To this end, the performance of two well‐known, state‐of‐the‐art evolutionary algorithms for feature selection (evolutionary nondominated radial slots based algorithm and nondominated sorted genetic algorithm) is compared for the task of feature selection, under the criteria of accuracy and cardinality of the selection, as well as for the task of fuzzy rule extraction, under the criteria of interpretability, accuracy, and hypervolume test. The best obtained fuzzy classifier, chosen after a decision making process, is validated and interpreted by domain experts.

ACS Style

Andrea Brunello; Fernando Jiménez; Enrico Marzano; Angelo Montanari; Gracia Sánchez; Guido Sciavicco. Multiobjective evolutionary feature selection and fuzzy classification of contact centre data. Expert Systems 2019, 36, e12375 .

AMA Style

Andrea Brunello, Fernando Jiménez, Enrico Marzano, Angelo Montanari, Gracia Sánchez, Guido Sciavicco. Multiobjective evolutionary feature selection and fuzzy classification of contact centre data. Expert Systems. 2019; 36 (3):e12375.

Chicago/Turabian Style

Andrea Brunello; Fernando Jiménez; Enrico Marzano; Angelo Montanari; Gracia Sánchez; Guido Sciavicco. 2019. "Multiobjective evolutionary feature selection and fuzzy classification of contact centre data." Expert Systems 36, no. 3: e12375.

Journal article
Published: 05 March 2019 in Computers
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Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. This paper presents J48SS, a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The novel algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm is evaluated on a text classification task in a real business setting, as well as on a selection of public UCR time series datasets. Results show that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort.

ACS Style

Andrea Brunello; Enrico Marzano; Angelo Montanari; Guido Sciavicco. J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data. Computers 2019, 8, 21 .

AMA Style

Andrea Brunello, Enrico Marzano, Angelo Montanari, Guido Sciavicco. J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data. Computers. 2019; 8 (1):21.

Chicago/Turabian Style

Andrea Brunello; Enrico Marzano; Angelo Montanari; Guido Sciavicco. 2019. "J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data." Computers 8, no. 1: 21.

Journal article
Published: 04 March 2019 in Information and Computation
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Interval temporal logics provide a natural framework for temporal reasoning about interval structures over linearly ordered domains, where intervals are taken as first-class citizens. Their expressive power and computational behaviour mainly depend on two parameters: the set of modalities they feature and the linear orders over which they are interpreted. In this paper, we consider all fragments of Halpern and Shoham's interval temporal logic HS with a decidable satisfiability problem over the rationals, and we provide a complete classification of them in terms of their expressiveness and computational complexity by solving the last few open problems.

ACS Style

D. Bresolin; D. Della Monica; A. Montanari; P. Sala; G. Sciavicco. Decidability and complexity of the fragments of the modal logic of Allen's relations over the rationals. Information and Computation 2019, 266, 97 -125.

AMA Style

D. Bresolin, D. Della Monica, A. Montanari, P. Sala, G. Sciavicco. Decidability and complexity of the fragments of the modal logic of Allen's relations over the rationals. Information and Computation. 2019; 266 ():97-125.

Chicago/Turabian Style

D. Bresolin; D. Della Monica; A. Montanari; P. Sala; G. Sciavicco. 2019. "Decidability and complexity of the fragments of the modal logic of Allen's relations over the rationals." Information and Computation 266, no. : 97-125.

Journal article
Published: 10 January 2019 in IEEE Transactions on Fuzzy Systems
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The interpretability of classification systems refers to the ability of these to express their behaviour in a way that is easily understandable by a user. Interpretable classification models allow for external validation by an expert and, in certain disciplines such as medicine or business, providing information about decision making is essential for ethical and human reasons. Fuzzy rule-based classification systems are consolidated powerful classification tools based on fuzzy logic and designed to produce interpretable models; however, in presence of a large number of attributes, even rule-based models tend to be too complex to be easily interpreted. In this work, we propose a novel multivariate feature selection method in which both search strategy and classifier are based on multi-objective evolutionary computation. We designed a set of experiments to establish an acceptable setting with respect to the number of evaluations required by the search strategy and by the classifier, and we tested our strategy on a real-life dataset. Then, we compared our results against a wide range of feature selection methods that includes filter, wrapper, multivariate and univariate methods, with deterministic and probabilistic search strategies, and with evaluators of diverse nature. Finally, the fuzzy rule based classification model obtained with the proposed method has been evaluated with standard performance metrics and compared with other well-known fuzzy rule-based classifiers. We have used two real-life datasets extracted from a contact center; in one case, with the proposed method we obtained an accuracy of 0.7857 with 8 rules, while the best fuzzy classifier compared obtained 0.7679 with 8 rules, and in the second case, we obtained an accuracy of 0.7403 with 5 rules, while the best fuzzy classifier compared obtained 0.6364 with 4 rules.

ACS Style

Fernando Jimenez; Carlos Martinez; Enrico Marzano; Jose Palma; Gracia Sanchez; Guido Sciavicco. Multiobjective Evolutionary Feature Selection for Fuzzy Classification. IEEE Transactions on Fuzzy Systems 2019, 27, 1085 -1099.

AMA Style

Fernando Jimenez, Carlos Martinez, Enrico Marzano, Jose Palma, Gracia Sanchez, Guido Sciavicco. Multiobjective Evolutionary Feature Selection for Fuzzy Classification. IEEE Transactions on Fuzzy Systems. 2019; 27 (5):1085-1099.

Chicago/Turabian Style

Fernando Jimenez; Carlos Martinez; Enrico Marzano; Jose Palma; Gracia Sanchez; Guido Sciavicco. 2019. "Multiobjective Evolutionary Feature Selection for Fuzzy Classification." IEEE Transactions on Fuzzy Systems 27, no. 5: 1085-1099.

Conference paper
Published: 18 December 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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Time series play a major role in many analysis tasks. As an example, in the stock market, they can be used to model price histories and to make predictions about future trends. Sometimes, information contained in a time series is complemented by other kinds of data, which may be encoded by static attributes, e.g., categorical or numeric ones, or by more general discrete data sequences. In this paper, we present J48SS, a novel decision tree learning algorithm capable of natively mixing static, sequential, and time series data for classification purposes. The proposed solution is based on the well-known C4.5 decision tree learner, and it relies on the concept of time series shapelets, which are generated by means of multi-objective evolutionary computation techniques and, differently from most previous approaches, are not required to be part of the training set. We evaluate the algorithm against a set of well-known UCR time series datasets, and we show that it provides better classification performances with respect to previous approaches based on decision trees, while generating highly interpretable models and effectively reducing the data preparation effort.

ACS Style

Andrea Brunello; Enrico Marzano; Angelo Montanari; Guido Sciavicco. A Novel Decision Tree Approach for the Handling of Time Series. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 351 -368.

AMA Style

Andrea Brunello, Enrico Marzano, Angelo Montanari, Guido Sciavicco. A Novel Decision Tree Approach for the Handling of Time Series. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():351-368.

Chicago/Turabian Style

Andrea Brunello; Enrico Marzano; Angelo Montanari; Guido Sciavicco. 2018. "A Novel Decision Tree Approach for the Handling of Time Series." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 351-368.

Journal article
Published: 17 October 2018 in Artificial Intelligence
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The primary characteristic of interval temporal logic is that intervals, rather than points, are taken as the primitive ontological entities. Given their generally bad computational behavior of interval temporal logics, several techniques exist to produce decidable and computationally affordable temporal logics based on intervals. In this paper we take inspiration from Golumbic and Shamir's coarser interval algebras, which generalize the classical Allen's Interval Algebra, in order to define two previously unknown variants of Halpern and Shoham's logic (HS) based on coarser relations. We prove that, perhaps surprisingly, the satisfiability problem for the coarsest of the two variants, namely HS3, not only is decidable, but PSpace-complete in the finite/discrete case, and PSpace-hard in any other case; besides proving its complexity bounds, we implement a tableau-based satisfiability checker for it and test it against a systematically generated benchmark. Our results are strengthened by showing that not all coarser-than-Allen's relations are a guarantee of decidability, as we prove that the second variant, namely HS7, remains undecidable in all interesting cases.

ACS Style

Emilio Muñoz-Velasco; Mercedes Pelegrín; Pietro Sala; Guido Sciavicco; Ionel Eduard Stan. On coarser interval temporal logics. Artificial Intelligence 2018, 266, 1 -26.

AMA Style

Emilio Muñoz-Velasco, Mercedes Pelegrín, Pietro Sala, Guido Sciavicco, Ionel Eduard Stan. On coarser interval temporal logics. Artificial Intelligence. 2018; 266 ():1-26.

Chicago/Turabian Style

Emilio Muñoz-Velasco; Mercedes Pelegrín; Pietro Sala; Guido Sciavicco; Ionel Eduard Stan. 2018. "On coarser interval temporal logics." Artificial Intelligence 266, no. : 1-26.

Preprint
Published: 12 September 2018
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ACS Style

Willem Conradie; Salih Durhan; Guido Sciavicco. An Integrated First-Order Theory of Points and Intervals over Linear Orders (Part II). 2018, 1 .

AMA Style

Willem Conradie, Salih Durhan, Guido Sciavicco. An Integrated First-Order Theory of Points and Intervals over Linear Orders (Part II). . 2018; ():1.

Chicago/Turabian Style

Willem Conradie; Salih Durhan; Guido Sciavicco. 2018. "An Integrated First-Order Theory of Points and Intervals over Linear Orders (Part II)." , no. : 1.

Journal article
Published: 07 September 2018 in Entropy
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The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be effortlessly related to the context of the problem, and their predictions can be, if necessary, ethically and legally evaluated. In this paper, we propose a novel method to generate rule-based classifiers from categorical data that can be readily interpreted. Classifiers are generated using a multi-objective optimization approach focusing on two main objectives: maximizing the performance of the learned classifier and minimizing its number of rules. The multi-objective evolutionary algorithms ENORA and NSGA-II have been adapted to optimize the performance of the classifier based on three different machine learning metrics: accuracy, area under the ROC curve, and root mean square error. We have extensively compared the generated classifiers using our proposed method with classifiers generated using classical methods such as PART, JRip, OneR and ZeroR. The experiments have been conducted in full training mode, in 10-fold cross-validation mode, and in train/test splitting mode. To make results reproducible, we have used the well-known and publicly available datasets Breast Cancer, Monk’s Problem 2, Tic-Tac-Toe-Endgame, Car, kr-vs-kp and Nursery. After performing an exhaustive statistical test on our results, we conclude that the proposed method is able to generate highly accurate and easy to interpret classification models.

ACS Style

Fernando Jiménez; Carlos Martínez; Luis Miralles-Pechuán; Gracia And Guido Sciavicco. Multi-Objective Evolutionary Rule-Based Classification with Categorical Data. Entropy 2018, 20, 684 .

AMA Style

Fernando Jiménez, Carlos Martínez, Luis Miralles-Pechuán, Gracia And Guido Sciavicco. Multi-Objective Evolutionary Rule-Based Classification with Categorical Data. Entropy. 2018; 20 (9):684.

Chicago/Turabian Style

Fernando Jiménez; Carlos Martínez; Luis Miralles-Pechuán; Gracia And Guido Sciavicco. 2018. "Multi-Objective Evolutionary Rule-Based Classification with Categorical Data." Entropy 20, no. 9: 684.