Incorporating logistic regression to decision-theoretic rough sets for classifications. (English) Zbl 1316.68185

Liu, Dun; Li, Tianrui; Liang, Decui

Int. J. Approx. Reasoning 55, No. 1, Part 2, 197-210 (2014).

Summary: Text of abstract logistic regression analysis is an effective approach to the classification problem. However, it may lead to high misclassification rate in real decision procedures. Decision-Theoretic Rough Sets (DTRS) employs a three-way decision to avoid most direct misclassification. We integrate logistic regression and DTRS to provide a new classification approach. On one hand, DTRS is utilized to systematically calculate the corresponding thresholds with Bayesian decision procedure. On the other hand, logistic regression is employed to compute the conditional probability of the three-way decision. The empirical studies of corporate failure prediction and high school program choices’ prediction validate the rationality and effectiveness of the proposed approach.

MSC:

68T37	Reasoning under uncertainty in the context of artificial intelligence
62H30	Classification and discrimination; cluster analysis (statistical aspects)
68T05	Learning and adaptive systems in artificial intelligence

Keywords:

decision-theoretic rough sets; binary logistic analysis; multivariate logistic regression; decision making