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R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification by Rubul Kumar Bania; Anindya Halder is a Computer Science article available to read on EtoBox.

What is R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification about?

Feature selection is one of the trustworthy processes of dimensionality reduction technique to select a subset of relevant and non-redundant features from large datasets. Ensemble feature selection (EFS) approach is a recent technique aiming at accumulating diversity in the subset of selected features. It improves the performance of learning algorithms and obtains more stable and robust results. In this paper, a novel rough set theory (RST) based heterogeneous EFS method (R-HEFS) is proposed for selecting the less redundant and highly relevant features during the aggregation of diverse feature subsets by applying the feature-class, feature-feature rough dependency and feature-significance measures. In R-HEFS five state-of-the-art RST based filter methods are used as a base feature selectors. Experiments are carried out on 10 benchmark medical datasets collected from the UCI repository. For the imputation of the missing values and discretization of the continuous features, k nearest neighbor (kNN) imputation method and RST based discretization techniques are applied. The effectiveness of the proposed R-HEFS method is evaluated and analyzed by using four benchmark classifiers viz., N

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Author

Rubul Kumar Bania; Anindya Halder

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)