Abstract
The following study is aimed at dealing with large volumes of data whose main characteristic is to contain a high number of variables, most of which are categorical in nature. In the knowledge extraction process, Knowledge Discovery in Databases (KDD), it is very common to deal with a stage of data pre-processing and dimensionality reduction. A key part of extracting information is having high quality data. This paper proposes the use of the Pairwise and Listwise methods as part of the dimensionality reduction process, when there is a high level of missing values present in one or more variables. As part of the pre-processing, we generate n-clusters using Kohonen Self-Organizing Maps (SOM) algorithm with H2O on R. A comparison of the performance and accuracy of classification algorithms is made with the complete subdata set and the algorithms are applied to each cluster. As a case study, we analyzed the characteristics that influence the level of schooling of women of childbearing age.
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Ruiz-Chavez, Z., Salvador-Meneses, J., Garcia-Rodriguez, J. (2018). Machine Learning Methods Based Preprocessing to Improve Categorical Data Classification. 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 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_32
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DOI: https://doi.org/10.1007/978-3-030-03493-1_32
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