NDLI: Systematic literature review of preprocessing techniques for imbalanced data

Content Provider	IET Digital Library
Author	Felix, Ebubeogu Amarachukwu Lee, Sai Peck
Abstract	Data preprocessing remains an important step in machine learning studies. This is because proper preprocessing of imbalanced data can enable researchers to reduce defects as much as possible, which, in turn, may lead to the elimination of defects in existing data sets. Despite the remarkable achievements that have been accomplished in machine learning studies, systematic literature reviews of imbalanced data preprocessing techniques are lacking. Consequently, there are a limited number of systematic literature review studies on imbalanced data preprocessing. In this study, the authors assess the existing literature to identify the key issues related to data quality and handling and to provide a convenient collection of the techniques used to address these issues when performing data preprocessing. They applied a systematic literature review method involving a manual search to select articles published from January 2010 to September 2018 for review. The qualities of the existing studies were assessed using certain quality assessment criteria. Of the 118 relevant studies found, only 2% were identified as having been conducted following systematic literature review guidelines. This study, therefore, calls for more systematic literature review studies on data preprocessing to improve the quality of the data applied in machine learning studies.
Starting Page	479
Ending Page	496
Page Count	18
ISSN	17518806
Volume Number	13
e-ISSN	17518814
Issue Number	Issue 6, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-sen/13/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-sen.2018.5193
Journal	IET Software
Publisher Date	2019-07-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Data Quality Data Sets Defect Reduction Imbalanced Data Preprocessing Technique Knowledge Engineering Technique Learning in AI Machine Learning Quality Assessment Criteria Review Systematic Literature Review GuideLine Systematic Literature Review Method
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

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