FE-CIDIM: fast ensemble of CIDIM classifiers

• Published in: International journal of systems science Vol. 37; no. 13; pp. 939 - 947
• Main Authors: Ramos-jiménez, Gonzalo, Campo-Ávila, José del, Morales-Bueno, Rafael
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 20.10.2006
• Subjects: CIDIM; Machine Learning; Multiple classifier systems
• ISSN: 0020-7721; 1464-5319
• DOI: 10.1080/00207720600891596

An active research area in machine learning is the construction of multiple classifier systems to increase learning accuracy of simple classifiers. In this article, we present E-CIDIM, a multiple classifier system designed to improve the performance of CIDIM, and FE-CIDIM, an algorithm developed to speed up E-CIDIM. CIDIM is an algorithm that induces small and accurate decision trees. E-CIDIM keeps a maximum number of trees and induces new trees that may substitute the old trees in the ensemble. The substitution process finishes when none of the new trees improves the accuracy of any of the trees in the ensemble after a preconfigured number of attempts. FE-CIDIM has been developed to speed up the convergence of E-CIDIM using a more restrictive substitution method. We will show that the accuracy obtained thanks to a unique instance of CIDIM can be improved utilizing these new multiple classifier systems.