Dynamic Ensemble Selection by K-Nearest Local Oracles with Discrimination Index

Abstract

This work describes a new oracle based Dynamic Ensemble Selection (DES) method in which an Ensemble of Classifiers (EoC) is selected to predict the class of a given test instance (xt). The competence of each classifier is estimated on a local region (LR) of the feature space (Region of Competence - RoC) represented by the most promising k-nearest neighbors (or advisors) related to xt according to a discrimination index (D) originally proposed in the Item and Test Analysis (ITA) theory. The D value is used to better define the advisors of the RoC since they will suggest the classifiers (local oracles) to compose the EoC. A robust experimental protocol based on 30 classification problems and 20 replications have shown that the proposed DES compares favorably with 15 state-of-the-art dynamic selection methods and the combination of all classifiers in the pool.