TY  - JOUR
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.
VL  - 51
IS  - 3
SN  - 0033-0337
DO  - 10.1108/PROG-02-2016-0014
UR  - https://doi.org/10.1108/PROG-02-2016-0014
AU  - Villuendas-Rey Yenny
AU  - Rey-Benguría Carmen
AU  - Lytras Miltiadis
AU  - Yáñez-Márquez Cornelio
AU  - Camacho-Nieto Oscar
PY  - 2017
Y1  - 2017/01/01
TI  - Simultaneous instance and feature selection for improving prediction in special education data
T2  - Program
PB  - Emerald Publishing Limited
SP  - 278
EP  - 297
Y2  - 2024/04/19
ER  -