Probabilistic approaches have been applied to the theory of rough set in several forms,
including decision-theoretic analysis, variable precision analysis, and information-theoretic …

Decision makers are often faced with several conflicting alternatives. How do they evaluate
trade-offs when there are more than three criteria? To help people make optimal decisions …

Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …

This study provides a comprehensive overview of the publications in Information Sciences
(INS) from 1968 to 2016 inclusive, which encompasses the history of this journal from its …

The original rough set model was developed by Pawlak, which is mainly concerned with the
approximation of sets described by a single binary relation on the universe. In the view of …

Tumor classification based on gene expression levels is important for tumor diagnosis.
Since tumor data in gene expression contain thousands of attributes, attribute selection for …

Many real data increase dynamically in size. This phenomenon occurs in several fields
including economics, population studies, and medical research. As an effective and efficient …

Rough set theory can be applied to rule induction. There are two different types of
classification rules, positive and boundary rules, leading to different decisions and …

When tackling high dimensionality in data mining, online feature selection which deals with
features flowing in one by one over time, presents more advantages than traditional feature …

Data reduction plays an important role in machine learning and pattern recognition with a
high-dimensional data. In real-world applications data usually exists with hybrid formats, and …