Incorporating logistic regression to decision-theoretic rough sets for classifications
Incorporating logistic regression to decision-theoretic rough sets for classifications
复制标题
将逻辑回归纳入决策理论粗糙集以进行分类
DOI：
10.1016/j.ijar.2013.02.013
复制
发表时间：
2014-01-01
影响因子：
3.9
通讯作者：
Liang, Decui
中科院分区：
文献类型：
--
作者：
Liu, Dun;Li, Tianrui;Liang, Decui
Logistic回归分析是解决分类问题的有效方法。然而，在实际的决策过程中，这可能会导致较高的误识率。决策理论粗糙集(DTRS)采用三向决策来避免最直接的错误分类。我们将Logistic回归和DTRS结合起来，提供了一种新的分类方法。一方面，通过贝叶斯决策过程，利用动态决策树系统地计算相应的阈值。另一方面，利用Logistic回归计算三向决策的条件概率。通过对企业失败预测和高中课程选择预测的实证研究，验证了该方法的合理性和有效性。(C)2013 Elsevier Inc.保留所有权利。
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. (C) 2013 Elsevier Inc. All rights reserved.