Forthcoming and Online First Articles

International Journal of Intelligent Information and Database Systems

International Journal of Intelligent Information and Database Systems (IJIIDS)

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 Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Intelligent Information and Database Systems (1 paper in press)

Regular Issues

  • An adaptive fuzzy weight algorithm for the class imbalance learning problem   Order a copy of this article
    by Vo Duc Quang, Tran Dinh Khang 
    Abstract: In this study, we propose an adaptive fuzzy weight algorithm for the problem of two-class imbalanced learning. Initially, our algorithm finds a set of fuzzy weight values for data samples based on the distance from each sample to the centres of both minority and majority classes. Then, our algorithm iteratively adjusts the fuzzy weight values of sensitive samples on either positive or negative margins or class label noises. By doing so, our algorithm increases the influence of minority samples and decreases the influence of majority samples in forming a classifier model. Experimental results on four benchmark real-world imbalanced datasets including Transfusion, Ecoli, Yeast, and Abalone show that our algorithm outperforms the fuzzy SVM-CIL algorithm in terms of classification performance.
    Keywords: classification algorithm; class imbalance learning; CIL; fuzzy support vector machines; FSVM; weighted support vector machines; WSVM; support vector machine; SVM.
    DOI: 10.1504/IJIIDS.2023.10058648