Abstract
As an important part of data preprocessing in machine learning and data mining, feature selection, also known as attribute reduction in rough set theory, is the process of choosing the most informative subset of features. Rough set theory has been used as such a tool with much success. The main objective of this paper is to propose a feature selection procedure based on a special group of probabilistic rough set models, called confirmation-theoretic rough set model(CTRS). Different from the existing attribute reduction methods, the definition of positive features is based on Bayesian confirmation measures. The proposed method is further divided into two categories based on the qualitative and quantitative nature of the underlying rough set models. This study provides new insights into the problem of attribute reduction.
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Zhou, B., Yao, Y. (2014). Feature Selection Based on Confirmation-Theoretic Rough Sets. In: Cornelis, C., Kryszkiewicz, M., Ślȩzak, D., Ruiz, E.M., Bello, R., Shang, L. (eds) Rough Sets and Current Trends in Computing. RSCTC 2014. Lecture Notes in Computer Science(), vol 8536. Springer, Cham. https://doi.org/10.1007/978-3-319-08644-6_19
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DOI: https://doi.org/10.1007/978-3-319-08644-6_19
Publisher Name: Springer, Cham
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