Forthcoming and Online First Articles

International Journal of Innovative Computing and Applications

International Journal of Innovative Computing and Applications (IJICA)

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International Journal of Innovative Computing and Applications (1 paper in press)

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  • An ensemble framework of decision trees for class imbalance using partitioning   Order a copy of this article
    by Vijayakumar Kadappa, Shankru Guggari, Rajeshwari Devi D. V 
    Abstract: Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced data is an active research area in many applications of machine learning. Feature set partitioning paradigm is proven to be effective for classification by many researchers. In this paper, we extend the ideas of partitioning to propose a decision tree ensemble to overcome class-imbalance issues. The framework consists of balancing the data, partitioning the feature set, building local decision trees, then combining decisions. Some instances of the proposed framework (e.g., Ferrer diagram-based approach) are used to validate the performance of the framework. An empirical study is carried out using University of California Irvine (UCI), Knowledge Extraction Evolutionary Learning (KEEL), and other datasets. The performance of the instances of the proposed framework demonstrates improved performance compared to other benchmark machine learning approaches.
    Keywords: machine learning; decision tree; feature set partitioning; class imbalance; ensemble method; classification.
    DOI: 10.1504/IJICA.2024.10063631