Application of the Gravitational Search Algorithm for Constructing Fuzzy Classifiers of Imbalanced Data

Abstract

The presence of imbalance in data significantly complicates the classification task, including fuzzy systems. Due to a large number of instances of bigger classes, instances of smaller classes are not recognized correctly. Therefore, additional tools for improving the quality of classification are required. The most common methods for handling imbalanced data have several disadvantages. For example, methods for generating additional instances of minority classes can worsen classification if there is a strong overlap of instances from different classes. Methods that directly modify the fuzzy classification algorithm lead to a decline in the interpretability of the model. In this paper, we study the efficiency of the gravitational search algorithm in the tasks of selecting the features and tuning the term parameters for fuzzy classifiers of imbalanced data. We consider only data with two classes and apply the algorithm based on extreme values of classes to construct models with a minimum number of rules. In addition, we propose a new quality metric based on the sum of the overall accuracy and the geometric mean with the presence of a priority coefficient between them.

Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions

“…The authors of [5,15,16] propose a method for constructing a fuzzy-rule-based classifier using the Gravity Search Algorithm (GSA). This article highlights three main stages of building a classifier: creation of a base of fuzzy rules, features selection, and optimization of the rules' parameters.…”

“…That means it is necessary to find a subset of features that does not lead to a significant loss of information about the object that allows it to be classified. The solution is represented as a vector S = (s 1 , s 2 , ...s d ), where d is the initial number of features in the dataset; if s i = 0, that means feature i is not used when classifying the object, and if s i = 1, that means the classifier uses feature i [5].…”

“…However, datasets on network connections are usually imbalanced (for example, the KDD Cup 1999 set). This complicates the process of constructing the classifier [5].…”