Feature Selection: Filter Methods Performance Challenges

Marianne Cherrington, Fadi Thabtah, Joan Lu, Qiang Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

51 Citations (Scopus)


Learning is the heart of intelligence. The focus in machine learning is to automate methods that achieve objectives, improve predictions or encourage informed behavior. Feature selection is a vital step in data analysis that often reduces dataset dimensionality by eliminating irrelevant and/or redundant attributes to simplify the learning process or improve outcomes' quality. This research critically analyses different filter methods based on ranking procedures (Information Gain (IG), Chi-square (CHI), V-score, Fisher Score, mRMR, Va and ReliefF) and identifies possible challenges that arise. We particularly concentrate on how threshold determination can affect results of different filter methods based on ranked scores. We show that this issue is vital, especially in the era of big data in which users deal with attributes in the magnitudes of tens of thousands with only a limited number of instances.

Original languageEnglish
Title of host publication2019 International Conference on Computer and Information Sciences, ICCIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781538681251
ISBN (Print)9781538681268
Publication statusPublished - 16 May 2019
Event2019 International Conference on Computer and Information Sciences - Sakaka, Saudi Arabia
Duration: 3 Apr 20194 Apr 2019


Conference2019 International Conference on Computer and Information Sciences
Abbreviated titleICCIS 2019
Country/TerritorySaudi Arabia
Internet address


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