Incremental feature selection approaches can improve the efficiency of feature selection
used for dynamic datasets, which has attracted increasing research attention. Nevertheless …

Incremental learning strategy based feature selection approaches can improve the efficiency
of reduction algorithm used for datasets with dynamic characteristic, which has attracted …

Feature selection is a popular technique of preprocessing data. In order to deal with
dynamic or large data, incremental feature selection has been developed, in which the …

Feature selection based on fuzzy rough sets is an effective approach to select a compact
feature subset that optimally predicts a given decision label. Despite being studied …

A fuzzy rough set is an important rough set model used for feature selection. It uses the fuzzy
rough dependency as a criterion for feature selection. However, this model can merely …

Incremental feature selection can improve learning of accumulated data. We focus on
incremental feature selection based on rough sets, which along with their generalizations …

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 …

Feature selection plays a vital role in many areas of pattern recognition and data mining.
The effective computation of feature selection is important for improving the classification …

In real-applications, there may exist many kinds of data (eg, boolean, categorical, real-
valued and set-valued data) and missing data in an information system which is called as a …

Rough set theory has been extensively discussed in machine learning and pattern
recognition. It provides us another important theoretical tool for feature selection. In this …