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Redundant feature identification and redundancy analysis for causal feature selection

Limshuebchuey, Asavaron, Duangsoithong, Rakkrit and Windeatt, Terry (2016) Redundant feature identification and redundancy analysis for causal feature selection In: 2015 8th Biomedical Engineering International Conference (BMEiCON), 25-27 November 2015, Pattaya, Thailand.

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High dimensional data can lead to low accuracy of classification and take a long time to calculate because it contains irrelevant features and redundant features. To overcome this problem, dimension of data has to be reduced. Causal feature selection is one of methods for feature reduction but it cannot identify redundant features. This paper presents Parent-Children based for Causal Redundant Feature Identification (PCRF) algorithm to identify and remove redundant features. The accuracy of classification and number of feature reduced by PCRF algorithm are compared with correlation feature selection. According to the results, PCRF algorithm can identify redundant feature but has lower accuracy of classification than correlation feature selection.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Limshuebchuey, Asavaron
Duangsoithong, Rakkrit
Date : 8 February 2016
DOI : 10.1109/BMEiCON.2015.7399532
Copyright Disclaimer : © 20x16 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : feature selection, correlation, causal feature selection, irrelevant feature, redundant feature
Depositing User : Melanie Hughes
Date Deposited : 01 Aug 2018 17:18
Last Modified : 01 Aug 2018 17:20

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