Classification of Imbalanced Data Based on MTS-CBPSO Method: A Case Study of Financial Distress Prediction
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초록
The traditional classification methods mostly assume that the data for class distribution is balanced, whileimbalanced data is widely found in the real world. So it is important to solve the problem of classification withimbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructedwith the reference space and measurement reference scale which is come from a single normal group, and thusit is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO isconstructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead oforthogonal 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 alsoapplied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTSand the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO methodhas better result of prediction accuracy and dimensionality reduction.
Abstract
The traditional classification methods mostly assume that the data for class distribution is balanced, whileimbalanced data is widely found in the real world. So it is important to solve the problem of classification withimbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructedwith the reference space and measurement reference scale which is come from a single normal group, and thusit is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO isconstructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead oforthogonal 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 alsoapplied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTSand the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO methodhas better result of prediction accuracy and dimensionality reduction.
참고문헌 (16)
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