A new approach for solving the binary imbalanced data problem using neural gas network

The objective of this study is to address the challenge of imbalanced data in data mining. To solve the problem, different methods are used to increase the minority class or reduce the majority class. However, reducing the number of samples eliminates important information, and increasing the number of samples can grow, increasing the computational cost and producing synthetic noise samples. In order to address the problem, we propose a technique that utilizes Gas Neural Network to uncover the topology of the distribution of the minority class. This approach aims to decrease the likelihood of generating synthetic samples, which results in additional overhead. We evaluated our method by comparing it with seven other widely recognized oversampling methods, not only on multiple online datasets from the Keel repository (consisting of 13 datasets) but also on two locally collected datasets to ensure the proposed method's independence from data collection methods. To evaluate the performance of the proposed method, we employ five different classifiers, including MLP, SVM, LVQ, RBFN, and C4.5. Furthermore, we compared the proposed method with seven different approaches for dealing with imbalanced datasets, including ADASYN, ADOMS, AHC, Borderline, SMOTE, ROD, and SPIDER. The performance is measured using the Area Under the Curve (AUC) metric. Experimental results demonstrate that our method outperforms conventional methods in classifying imbalanced datasets. Moreover, the process is robust enough to perform better on datasets that have been locally collected by ourselves. The proposed method provides a comprehensive solution to the challenge of imbalanced data in data mining. By utilizing the Neural Gas Network and uncovering the topology of the minority class distribution, it efficiently diminishes the necessity of generating synthetic samples. The experimental results emphasize the superiority of our method in classifying imbalanced datasets compared to conventional approaches.