Abstract
An important step in the knowledge discovery in databases (KDD) process is the attribute selection procedure, which aims at choosing a subset of attributes that can represent the important information within the data. Most of the existing attribute selection methods can only handle simple attribute types, such as categorical and numerical. In particular, these methods cannot be applied to multivalued attributes, which are attributes that take multiple values simultaneously for the same instance in the dataset. This article proposes two relevance measures for multivalued attributes, which aim at measuring their importance for classification. The proposed measures are adaptations of two widely used relevance measures for categorical attributes: information gain and gain ratio. In order to evaluate the proposed measures, experiments were conducted with multiclass datasets submitted to multi-relational classifiers. The experiments show that the proposed measures are good indicators of the relevance of multivalued attributes for multiclass classification.
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Tasca, M., Zadrozny, B., Plastino, A. (2014). Relevance Measures for Multivalued Attributes in Multiclass Datasets. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_28
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DOI: https://doi.org/10.1007/978-3-319-12027-0_28
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