Forthcoming and Online First Articles

International Journal of Data Science

International Journal of Data Science (IJDS)

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 Articles will not be published until the final proofs are validated by their authors.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 Data Science (One paper in press)

Regular Issues

  • A Commensurate Univariate Variable Ranking Method for Classification   Order a copy of this article
    by Nuo Xu, Xuan Huang, Thanh Nguyen, Jake Yue Chen 
    Abstract: To apply a variable ranking method for feature selection in classification, the notion of commensurateness is necessitated by the presence of different types of independent variables in a dataset. A commensurate ranking method is one that produces consistent and comparable ranking results among independent variables of different types, such as numeric vs categorical and discrete vs continuous. We invent a ranking method named Condition Empirical Expectation (CEE) and demonstrate it is the most commensurate among several representative ranking methods. Further, it has the highest statistical power as a test of independence when the categorical dependent variable is imbalanced. These properties make CEE uniquely suitable for fast feature selection for any datasets, especially those with high dimensionality of mixed types of variables. Its usage is demonstrated with a case study in facilitating preprocessing for classification.
    Keywords: variable types; variable ranking; variable relevance; commensurate; statistical dependence.
    DOI: 10.1504/IJDS.2025.10067405
     

 

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