Ensemble meta classifier with sampling and feature selection for data with multiclass imbalance problem. Journal of Information and Communication Technology, 20 (2). pp. 103-133. ISSN 2180-3862 (2021)
Abstract
Ensemble learning by combining several single classifiers or another ensemble classifier is one of the procedures to solve the imbalance problem in multiclass data. However, this approach still faces the question of how the ensemble methods obtain their higher performance. In this paper, an investigation was carried out on the design of the meta classifier ensemble with sampling and feature selection for multiclass imbalanced data. The specific objectives were: 1) to improve the ensemble classifier through data-level approach (sampling and feature selection); 2) to perform experiments on sampling, feature selection, and ensemble classifier model; and 3 ) to evaluate t he performance of the ensemble classifier. To fulfil the objectives, a preliminary data collection of Malaysian plants’ leaf images was prepared and experimented, and the results were compared.
| Item Type: | Article |
|---|---|
| Keywords: | Data science, Binary classification, Data mining, Ensemble classifier, Algorithm, Classification |
| Taxonomy: | By Subject > Computer & Mathematical Sciences > Computer Science |
| Local Content Hub: | Subjects > Computer and Mathematical Sciences |
| Depositing User: | Muslim Ismail @ Ahmad |
| Date Deposited: | 19 Feb 2022 00:00 |
| Last Modified: | 22 Feb 2022 01:48 |
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