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Open AccessArticle

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

Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, 634050 Tomsk, Russia
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Symmetry 2019, 11(12), 1458; https://doi.org/10.3390/sym11121458
Received: 28 September 2019 / Revised: 28 October 2019 / Accepted: 24 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Information Technologies and Electronics)
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.
Keywords: fuzzy classifiers; gravitational search algorithm; imbalanced data; geometric mean; feature selection fuzzy classifiers; gravitational search algorithm; imbalanced data; geometric mean; feature selection
MDPI and ACS Style

Bardamova, M.; Hodashinsky, I.; Konev, A.; Shelupanov, A. Application of the Gravitational Search Algorithm for Constructing Fuzzy Classifiers of Imbalanced Data. Symmetry 2019, 11, 1458.

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