Aiming at the problems that traditional data mining methods ignore inconsistent data, and
general decision tree learning algorithms lack of theoretical support for the classification of …

Attribute reduction is an important method in data analysis and machine learning, and it
usually relies on algebraic and informational measures. However, few existing informational …

Decision-theoretic rough set is a special rough set approach, which includes both
misclassification and delayed decision costs. Though the property of monotonicity does not …

Decision‐theoretic rough set is a quite useful rough set by introducing the decision cost into
probabilistic approximations of the target. However, Yao's decision‐theoretic rough set is …

When removing some attributes, the partition induced by a smaller set of attributes will be
coarser and the decision regions may be changed. In this paper, we analyze the decision …

Many attribute reduction methods have been proposed for decision-theoretic rough set
model based on different definitions of attribute reduct, while an attribute reduct can be seen …

The AGM postulates 1 are for the belief revision (revision by a single belief), and the DP
postulates 14 are for the iterated revision (revision by a finite sequence of beliefs). Li 4 gave …

Çok kriterli karar verme problemi, çağımız yöneticilerinin sıklıkla başvurmuş olduğu
yöntemlerden birisidir. Verilerin belirsiz ya da eksik olması durumunda, mevcut olan çok …

Decision-theoretic rough set (DTRS) model, proposed by Yao in the early 1990's, introduces
Bayesian decision procedure and loss function in rough set theory. Considering utility …

Attribute reduction is one of the most important topics in rough set theory. Heuristic attribute
reduction algorithms have been presented to solve the attribute reduction problem. It is …