Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

• Published in: Symmetry (Basel) 2022-03, Vol.14 (3), p.600
• Main Authors: Masich, Igor S., Kulachenko, Margarita A., Stanimirović, Predrag S., Popov, Aleksey M., Tovbis, Elena M., Stupina, Alena A., Kazakovtsev, Lev A.
• Format: Article
• Language: English
• Subjects: Boolean; Classification; Data analysis; Decision trees; Evolutionary algorithms; Fuzzy logic; genetic algorithm; Genetic algorithms; Integer programming; Linear programming; logical analysis of data; Machine learning; Multiple criterion; Optimization; Optimization models; Pattern analysis; Pattern generation; Pattern search; Sorting algorithms
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym14030600

The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve this problem. The new algorithm uses pattern formation as an approximation of the Pareto set and considers the solution’s representation in logical analysis of data and the informativeness of patterns. We have tested our approach on two applied medical problems of classification under conditions of sample asymmetry: one class significantly dominated the other. The classification results were comparable and, in some cases, better than the results of commonly used machine learning algorithms in terms of accuracy, without losing the interpretability.