This paper also presents different feature evaluation criteria such as dependency, relevance, redundancy, and significance for attribute selection task using ...

This paper also presents different feature evaluation criteria such as dependency, relevance, redundancy, and significance for attribute selection task using ...

On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, Min-Redundancy, and Max-Significance. @article{Maji2013OnFA, title={On fuzzy ...

A feature selection method is presented here based on fuzzy-rough sets by maximizing both relevance and significance of the selected features. This paper also ...

Combining both Max-. Relevance and Min-Redundancy criteria, the mRMR incre- mental selection scheme provides a better way to maximize the dependency. In this ...

On Fuzzy-Rough Attribute Selection: Criteria of Max-Dependency, Max-Relevance, Min-Redundancy and Max-Significance. P Maji, P Garai. Applied Soft Computing 13 ...

Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance ...

On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, Min-Redundancy, and Max-Significance · Neighborhood rough set based heterogeneous ...

It selects a set of features from a high-dimensional data set by maximizing the relevance and significance of the selected features. A theoretical analysis is ...

Different feature evaluation criteria such as dependency, relevance, and significance are presented for attribute selection task using interval type-2 fuzzy- ...