Rough set theory is an efficient tool for dealing with inexact and uncertain information.
Numerous studies have focused on rough set theory and associated methodologies, and in …

Since different features may require different costs, the cost-sensitive feature selection
problem become more and more important in real-world applications. Generally, it includes …

Feature selection is an important data-preprocessing technique in classification problems
such as bioinformatics and signal processing. Generally, there are some situations where a …

Many previous studies on face recognition attempted to seek a precise classifier to achieve
a low misclassification error, which is based on an assumption that all misclassification costs …

As a natural extension of three-way decisions with incomplete information, this paper
provides a novel three-way decision model based on incomplete information system. First …

Recommender systems attempt to guide users in decisions related to choosing items based
on inferences about their personal opinions. Most existing systems implicitly assume the …

Abstract Three-way decision (3WD) models have been widely investigated in the fields of
approximate reasoning and decision making. Recently, sequential 3WD models have …

Similar to feature selection over completely labeled data, the aim of feature selection over
partially labeled data (semi-supervised feature selection) is also to find a feature subset …

In classical rough set models, attribute reduction generally keeps the positive or non-
negative regions unchanged, as these regions do not decrease with the addition of …

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 …