Feature Subset Selection based on Redundancy Maximized Clusters

Tarek, Md. Hasan; Kadir, Md. Eusha; Sharmin, Sadia; Sajib, Abu Ashfaqur; Ali, Amin Ahsan; Shoyaib, Mohammad

doi:10.1109/icmla52953.2021.00087

Cited by 1 publication

(2 citation statements)

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“…There are numerous sorts of feature selection/extraction techniques available to identify those features. Feature selection is a process of selecting relevant, important features F S and removing irrelevant features from a set of feature F. Relevant features selection and eliminating irrelevant features will reduce time and accelerate the classification performance [2], [3], [34], [35].…”

Section: Introductionmentioning

confidence: 99%

“…distance, consistency, dependency, correlation and mutual information (MI) [6], [9]. Among these measures, MI is more popular than others because of its ability to capture the non-linear and linear relation between features in the dataset and it can be used with categorical as well as numerical values [6], [9], [10], [34], [35]. Several MI based methods have been proposed over time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Clustering based Feature Selection Approach using Maximum Spanning Tree

Tarek¹,

Akhter²,

Ahmed

et al. 2023

Dhaka Uni. J. of Applied Sci. and Eng.

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Mutual information (MI) based feature selection methods are getting popular as its ability to capture the nonlinear and linear relationship among random variables and thus it performs better in different fields of machine learning. Traditional MI based feature selection algorithms use different techniques to find out the joint performance of features and select the relevant features among them. However, to do this, in many cases, they might incorporate redundant features. To solve these issues, we propose a feature selection method, namely Clustering based Feature Selection (CbFS), to cluster the features in such a way so that redundant and complementary features are grouped in the same cluster. Then, a subset of representative features is selected from each cluster. Experimental results of CbFS and four state-of-the-art methods are reported to measure the excellency of CbFS over twenty benchmark UCI datasets and three renowned network intrusion datasets. It shows that CbFS performs better than the comparative methods in terms of accuracy and performs better in identifying attack or normal instances in security datasets. DUJASE Vol. 7 (2) 47-55, 2022 (July)

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Section: Introductionmentioning

confidence: 99%