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Kashif Hussain
Universiti Tun Hussein Onn Malaysia

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Imbalance class problems in data mining: a review Haseeb Ali; Mohd Najib Mohd Salleh; Rohmat Saedudin; Kashif Hussain; Muhammad Faheem Mushtaq
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1552-1563

Abstract

The imbalanced data problems in data mining are common nowadays, which occur due to skewed nature of data. These problems impact the classification process negatively in machine learning process. In such problems, classes have different ratios of specimens in which a large number of specimens belong to one class and the other class has fewer specimens that is usually an essential class, but unfortunately misclassified by many classifiers. So far, significant research is performed to address the imbalanced data problems by implementing different techniques and approaches. In this research, a comprehensive survey is performed to identify the challenges of handling imbalanced class problems during classification process using machine learning algorithms. We discuss the issues of classifiers which endorse bias for majority class and ignore the minority class. Furthermore, the viable solutions and potential future directions are provided to handle the problems.
Co-Authors Haseeb Ali Mohd Najib Mohd Salleh Muhammad Faheem Mushtaq Rd. Rohmat Saedudin