Abstract
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.
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 language | English |
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Title of host publication | 2021 22nd International Arab Conference on Information Technology (ACIT) |
Publisher | IEEE Industrial Electronics Society |
Pages | 199-209 |
Number of pages | 11 |
ISBN (Electronic) | 978-1-6654-1995-6 |
ISBN (Print) | 978-1-6654-1996-3 |
DOIs | |
Publication status | Published - Apr 2022 |
Event | International Arab Conference on Information Technology (ACIT'2021) - Sultan Qaboos University , Al Khoudh, Oman Duration: 21 Dec 2021 → 23 Dec 2021 Conference number: 22nd https://acit2k.org/ACIT/index.php |
Conference
Conference | International Arab Conference on Information Technology (ACIT'2021) |
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Abbreviated title | ACIT'2021 |
Country/Territory | Oman |
City | Al Khoudh |
Period | 21/12/21 → 23/12/21 |
Internet address |
Keywords
- Stability of Feature Selection
- Class Overlapping
- Data Characteristics
- Complex Data