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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
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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

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.