High-dimensional datasets often harbor redundant, irrelevant, and noisy features that detrimentally impact classification algorithm performance. Feature selection (FS) aims to mitigate this issue by identifying and retaining only the most pertinent features, thus reducing dataset dimensions. In this study, we propose an FS approach based on black hole algorithms (BHOs) augmented with a mutation technique termed MBHO. BHO typically comprises two primary phases. During the exploration phase, a set of stars is iteratively modified based on existing solutions, with the best star selected as the “black hole”. In the exploration phase, stars nearing the event horizon are replaced, preventing the algorithm from being trapped in local optima. To address the potential randomness-induced challenges, we introduce inversion mutation. Moreover, we enhance a widely used objective function for wrapper feature selection by integrating two new terms based on the correlation among selected features and between features and classification labels. Additionally, we employ a transfer function, the V2 transfer function, to convert continuous values into discrete ones, thereby enhancing the search process. Our approach undergoes rigorous evaluation experiments using fourteen benchmark datasets, and it is compared favorably against Binary Cuckoo Search (BCS), Mutual Information Maximization (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Eelevance (mRMR), approaches. The results demonstrate the efficacy of our proposed model in selecting superior features that enhance classifier performance metrics. Thus, MBHO is presented as a viable alternative to the existing state-of-the-art approaches. We make our implementation source code available for community use and further development.

Interdependent systems, with multiple interconnected assets, face escalating cybersecurity threats from external attackers. This article explores security decision-making, operating on complex interdependent systems and proposes a security resource allocation methodology to enhance their proactive security. Using attack graphs, we model vulnerabilities and propose different defense mechanisms integrating different network analysis algorithms, including degree, betweenness, and harmonic centralities, TrustRank, and Katz centrality. We introduce Average Based Node Ranking (ABNR) to average ranks from these methods. The resource allocation methods leverage four different graph-theoretic methods. Each ranking algorithm is combined with these four allocation techniques. Our methods show low sensitivity to simultaneous attacks on interdependent systems. We validate our framework using 11 attack graphs representing real-world systems, measuring security improvements against four well-known allocation algorithms: behavioral decision-making, defense-in-depth, risk-based defense, and min-cut. Our framework outperformed the baselines in most cases, with superior outcomes confirmed by the Friedman statistical test. We show that the main components in our framework have low-time overhead. We also evaluate our framework against multi-stage attacks and cascading failures Our framework enhances security decision-making across different scenarios, including top-1 and all attack paths for different attacks. We release the implementation of our resource allocation methodology to the research community

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

Computer vision plays a crucial role in current day tasks due to its wide range of merits and applications in many fields like disease diagnosis, medical decisions, military, security, scientific applications, and business. Identifying plant species upon their leaf images is a very challenging and complex task. In this study, we introduced a model for classifying a variety set of plant leaves using different techniques such as factorization machine (FM), dimensionality reduction (DR), and ensemble learning (EL). In our study, FM is used as a classification algorithm, whereas DR is included in two stages, namely, feature extraction (FE) and feature selection (FS). FE has been used to extract the features from images in tabular form; on the other hand, FS is used to reduce the size of the data by getting rid of noisy (redundant and irrelevant) features. In addition, we utilized two methods for FS, filter‐based and wrapper‐based approaches. The used filter was the minimum redundancy maximum relevance, and the used wrapper was the improved binary cuckoo search algorithm that is based on Lévy flight and abandon nest functions. Regarding EL, it was used to declare different versions of FM by passing different subsets of features that are deduced from the original ones to improve its performance. We used the Swedish and Flavia leaf datasets for training and testing phases. The proposed model achieved a high performance in terms of accuracy that produced 95.67% on Swedish dataset and 99.6% on Flavia dataset. According to the results and in comparison to other methods, we proved that our proposed model ensures a favorable plant leaf classification approach. Finally, the proposed wrapper FS approach showed very good results without setting up the number of features to be selected by the user. The entire implementation of this work can be found at https://github.com/Mohammed-Ryiad-Eiadeh/Binary-Cuckoo-Search-For-Plant-Leaf-Prediction .