The purpose of this paper is to improve the classification of families having children with affective-behavioral maladies, and thus giving the families a suitable orientation.
The proposed methodology includes three steps. Step 1 addresses initial data preprocessing, by noise filtering or data condensation. Step 2 performs a multiple feature sets selection, by using genetic algorithms and rough sets. Finally, Step 3 merges the candidate solutions and obtains the selected features and instances.
The new proposal show very good results on the family data (with 100 percent of correct classifications). It also obtained accurate results over a variety of repository data sets. The proposed approach is suitable for dealing with non-symmetric similarity functions, as well as with high-dimensionality mixed and incomplete data.
Previous work in the state of the art only considers instance selection to preprocess the schools for children with affective-behavioral maladies data. This paper explores using a new combined instance and feature selection technique to select relevant instances and features, leading to better classification, and to a simplification of the data.
The authors would like to thank: the Instituto Politécnico Nacional (Secretaría Académica, COFAA, SIP, and CIC), the CONACyT and SNI, Mexico, for their economic support to develop this work.
Villuendas-Rey, Y., Rey-Benguría, C., Lytras, M., Yáñez-Márquez, C. and Camacho-Nieto, O. (2017), "Simultaneous instance and feature selection for improving prediction in special education data", Program: electronic library and information systems, Vol. 51 No. 3, pp. 278-297. https://doi.org/10.1108/PROG-02-2016-0014Download as .RIS
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