Authors:
Henrique R. Hott
1
;
Caroline R. Jandre
1
;
Pedro H. S. Xavier
1
;
Amal Miloud-Aouidate
2
;
Débora M. Miranda
3
;
Mark A. Song
1
;
Luis E. Zárate
1
and
Cristiane N. Nobre
1
Affiliations:
1
Department of Computer Science, Pontifical Catholic University of Minas Gerais University, Brazil
;
2
University of Sciences and Technology Houari Boumediene, Algeria
;
3
Department of Pediatrics, Federal University of Minas Gerais, Minas Gerais, Brazil
Keyword(s):
ADHD, Attention-Deficit/Hyperactivity Disorder, Instance Selection, Ant Colony.
Abstract:
Instance Selection (IS) helps select the most notable instances from the database, improving its characterization and relevance. In this context, this article applies the IS, using the Ant Colony Optimization (ACO) heuristic, to obtain more efficient classification models in the identification of school performance, in arithmetic, writing, and reading, of children and adolescents with Attention-Deficit/Hyperactivity Disorder (ADHD), characterized by excessive symptoms of inattention, hyperactivity, and impulsivity. The Random Forest, Neural Networks, KNN, and CART classifiers were used to evaluate the performance of the selection performed by the ACO method. With the ACO, it was possible to obtain a gain of 20 percentage points with the KNN (K = 1), in arithmetic, in the metric F-measure, referring to the upper class, the minority class. The results achieved show the excellent efficiency of the ACO in this study.