An ensemble classifier through rough set reducts for handling data with evidential attributes

Ensemble classifier is a well-known method that has been used to solve several machine learning problems. To have reliable results, one should ensure the build of a good ensemble. In order to do so, researchers have proposed some heuristics like Random Subspace Ensemble (RSM), Rough set bas. The drawback of these mentioned approaches is their disability to handle uncertain data especially when uncertainty is represented by the evidence theory. The aim of this paper is to adapt both RSM and Rough set based ensemble in order to let them working in the context of evidential data. Three ensemble classifier approaches based on the rough set theory have been proposed and have been compared with each other. For the comparison purpose, we have relied on Ensemble Enhanced Evidential k Nearest Neighbor (EEk-NN) classifier, real world datasets from the UCI repository as well as synthetic databases.

Automated summary

This paper explores how to adapt random subspace ensemble and rough set based ensemble methods for handling evidential data in machine learning problems. By proposing three ensemble classifier approaches based on rough set theory and comparing them with other methods, reliable results have been obtained.