Search Word(s) in Title, Keywords, Authors, and Abstract: Mahalanobis-Taguchi

Classification of Imbalanced Data Based on MTS-CBPSO Method: A Case Study of Financial Distress Prediction
Yuping Gu, Longsheng Cheng and Zhipeng Chang
Page: 682~693, Vol. 15, No.3, 2019
10.3745/JIPS.04.0119
Keywords: Chaotic Binary Particle Swarm Optimization (CBPSO), Financial Distress Prediction, Mahalanobis-Taguchi System (MTS), Variable Selection
Show / Hide Abstract
The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.