Abstract
This paper explores the limitation of consistency-based measures in the context of feature selection. These kinds of filters are not very widespread in large-dimensionality problems. Typically, the number of selected of attributes is very small and the ability to do right predictions is a drawback. The principal contribution of this work is the introduction of a new approach within feature engineering to create new attributes after the feature selection stage. The experimentation on multi-class problems with a feature space in the order of tens of thousands shed light on that some improvements took place with the new proposal. As a final insight, some new relationships were discovered due to the combined application of feature selection and feature transformation. Additionally, a new measure for classification problems which relates the number of features and the number of classes or labels is also proposed.
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Acknowledgment
This work has been partially subsidised by TIN2014-55894-C2-R, TIN2017-88209-C2-R (Spanish Inter-Ministerial Commission of Science and Technology (MICYT)), P11-TIC-7528 projects (“Junta de Andalucía” (Spain)) and FEDER funds. It has also been supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant no. III-44006.
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Tallón-Ballesteros, A.J., Tuba, M., Xue, B., Hashimoto, T. (2018). Feature Selection and Interpretable Feature Transformation: A Preliminary Study on Feature Engineering for Classification Algorithms. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_31
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