Stability and Accuracy of Feature Selection Methods on Datasets of Varying Data Complexity

Omaimah Saif Al Hosni* (Corresponding Author), Andrew Starkey

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

1 Citation (Scopus)


One widespread criterion used to evaluate feature selection techniques is the classifier performance of the selected features. Another criterion that has recently drawn attention in the feature selection community is the stability of feature selection techniques. Our study indicates that using feature selection
techniques with different data characteristics may generate different subsets of features under variations to the training data. Our study motivation is that there are significant contributions in the research community from examining the effect of complex data characteristics such as class overlap on classification algorithms performance; however, relatively few studies have investigated the stability and the accuracy of feature selection methods with complex data characteristics. Accordingly, this study aims to conduct empirical study to
measure the interactive effects of the class overlap with different data characteristics so we will provide meaningful insights into the root causes for feature selection methods misdiagnosing the relevant features among different data challenges associated with real world data in which will guide the practitioners and researchers to choose the correct feature selection methods that are more appropriate for particular dataset. Also, in this study we will provide a survey on the current state of research in the feature selection stability context.
Original languageEnglish
Title of host publication2021 22nd International Arab Conference on Information Technology (ACIT)
PublisherIEEE Industrial Electronics Society
Number of pages11
ISBN (Electronic)978-1-6654-1995-6
ISBN (Print)978-1-6654-1996-3
Publication statusPublished - Apr 2022
EventInternational Arab Conference on Information Technology (ACIT'2021) - Sultan Qaboos University , Al Khoudh, Oman
Duration: 21 Dec 202123 Dec 2021
Conference number: 22nd


ConferenceInternational Arab Conference on Information Technology (ACIT'2021)
Abbreviated titleACIT'2021
CityAl Khoudh
Internet address


  • Stability of Feature Selection
  • Class Overlapping
  • Data Characteristics
  • Complex Data


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