Forthcoming Articles

International Journal of Artificial Intelligence and Soft Computing

International Journal of Artificial Intelligence and Soft Computing (IJAISC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Artificial Intelligence and Soft Computing (One paper in press)

Regular Issues

  • Enhancing Class Separability in Imbalanced Learning: a Novel Model and Comparative Study   Order a copy of this article
    by Eric Jiang 
    Abstract: Class imbalance and other data complexity issues such as class overlap are common in many real-world applications. These collective challenges can lead to biased learning models and reduced predictive performance. Various strategies that combine data sampling with data cleaning have been developed to address these challenges by mitigating learning bias and enhancing class separability. This paper introduces a model-based selective under-sampling approach designed to identify and remove potentially noisy or unreliable instances and rebalance the class distribution of training data. In addition, the paper conducts a comprehensive comparative study on several widely used selective under-sampling methods and it involves extensive experiments on a diverse collection of 40 datasets from a broad range of applications. The study performs in-depth comparisons of the examined methods using a framework of non-parametric statistical tests and provides valuable empirical findings, insights and observations that may benefit machine learning researchers and practitioners.
    Keywords: class distribution; class overlap; data sampling; data cleaning; non-parametric statistical test.
    DOI: 10.1504/IJAISC.2026.10078600
     

 

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