TY - JOUR
T1 - Simultaneous instance and feature selection for improving prediction in special education data
AU - Villuendas-Rey, Yenny
AU - Rey-Benguría, Carmen
AU - Lytras, Miltiadis
AU - Yáñez-Márquez, Cornelio
AU - Camacho-Nieto, Oscar
N1 - Publisher Copyright:
© 2017, © Emerald Publishing Limited.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Classification
KW - Feature selection
KW - Instance selection
KW - Pattern recognition
KW - Prediction
KW - Special education
UR - http://www.scopus.com/inward/record.url?scp=85029349596&partnerID=8YFLogxK
U2 - 10.1108/PROG-02-2016-0014
DO - 10.1108/PROG-02-2016-0014
M3 - Artículo
SN - 0033-0337
VL - 51
SP - 278
EP - 297
JO - Program
JF - Program
IS - 3
ER -