Coastal cliffs are very sensitive to degradation caused by erosion and abrasion. Thus, it is very important to monitor susceptibility of the cliffs in terms of slope angles and ground fall resulting from vertical morphology of the cliffs. The results could be used for example to establish the boundaries of the safe investments zone or retreat infrastructure buildings in case of real threat such as degradation of the objects of interests, by creating a warning system from collected data. What is more, the world challenges constantly change and besides classic monitoring and prediction of degradation of the cliffs, it is very important to examine the state of the cliffs with coastal infrastructure in case of potential anthropogenic threat, e.g. war or terrorist attack.

The purpose of the study is to propose a multi-class ensemble classifier using interval modeling dedicated to microarray datasets. An approach of creating the uncertainty intervals for the single prediction values of constituent classifiers and then aggregating the obtained intervals with the use of interval-valued aggregation functions is used. The proposed heterogeneous classification employs Random Forest, Support Vector Machines, and Multilayer Perceptron as component classifiers, utilizing cross-entropy to select the optimal classifier. Moreover, orders for intervals are applied to determine the decision class of an object. The applied interval-valued aggregation functions are tested in terms of optimizing the performance of the considered ensemble classifier. The proposed model’s quality, superior to other well-known and component classifiers, is validated through comparison, demonstrating the efficacy of cross-entropy in ensemble model construction.

Feature selection is one of the key stages in the process of creating machine learning models and conducting data analysis. This paper presents the results of research related to the implementation of a new algorithm for feature selection and ranking based on weighted interval aggregations. It took into account interval importance values obtained from dividing the dataset into subsets. The algorithm was highly effective in identifying relevant features. The results of comparative studies with nine known methods of feature importance assessment are presented. Ten synthetic datasets and five real datasets were used for the experiments. The calculations also included tests of the relevance of the results obtained. In most experiments, the IVWFR algorithm proved to be the best, achieving the best classification results after identifying subsets of relevant features.