Paper Info
Title
Simultaneous instance and feature selection for improving prediction in special education data.
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.
Year
DOI
Venue
2017
10.1108/PROG-02-2016-0014
PROGRAM-ELECTRONIC LIBRARY AND INFORMATION SYSTEMS
Keywords
DocType
Volume
Pattern recognition,Classification,Feature selection,Prediction,Special education,Instance selection
Journal
51
Issue
ISSN
Citations 
3
0033-0337
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Yenny Villuendas-rey14614.38
Carmen Rey-Benguría241.77
Miltiadis D. Lytras31061123.44
Cornelio Yáñez-Márquez415326.34
Oscar Camacho Nieto56514.93