Simultaneous instance and feature selection for improving prediction in special education data. Issue 3 (5th September 2017)

• Record Type: Journal Article
• Authors: Villuendas-Rey, Yenny; Rey-Benguría, Carmen; Lytras, Miltiadis; Yáñez-Márquez, Cornelio; Camacho-Nieto, Oscar
• Is Part Of: Program. Volume 51:Issue 3(2017)
• Page Start: 278
• Page End: 297
• Publication Date: 2017-09-05
• Subjects: Pattern recognition -- Classification -- Feature selection -- Prediction -- Special education -- Instance selection; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-02-2016-0014
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.