A novel ensemble classifier by combining sampling and genetic algorithm to combat multiclass imbalanced problems

Archana Purwar and Sandeep Kumar Singh

International Journal of Data Analysis Techniques and Strategies, 2020, vol. 12, issue 1, 30-42

Abstract: To handle datasets with imbalanced classes is an exigent problem in the area of machine learning and data mining. Though a lot of work has been done by many researchers in the literature for two-class imbalanced problems, the multiclass problems still need to be explored. In this paper, we propose sampling and genetic algorithm based ensemble classifier (SA-GABEC) to handle imbalanced classes. SA-GABEC tries to find the best subset of classifiers for a given sample that is precise in predictions and can create an acceptable diversity in features subspace. These subsets of classifiers are fused together to give better predictions as compared to a single classifier. Moreover, this paper also proposes modified SA-GABEC which performs the feature selection before applying sampling and outperforms SA-GABEC. The performance of the proposed classifiers is evaluated and compared with GAB-EPA, Adaboost and bagging using minority class recall and extended G-mean.

Keywords: feature extraction; diversity; genetic algorithm; ensemble learning; multiclass imbalanced problems. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=105154 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:12:y:2020:i:1:p:30-42

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().