Subject: machine learning, prediction methods, pattern classification, decision-making
Year: 2011
Type: Proceeding article
Title: Probabilistic predictions of ensemble of classifiers combined with dynamically weighted majority vote
Author: Zdravevski, Eftim
Author: Kulakov, Andrea
Author: Kalajdziski, Slobodan
Author: Davchev, Dancho
Abstract: This paper presents a new method for dynamic calculation of weights that can be used in the process of aggregation of classifications by weighted majority vote. The proposed method can be used for all binary classification problems for classifiers that produce probabilistic classifications. Most aggregation functions produce an output which only represents the aggregated classification of an ensemble of classifiers and sometimes this isn't enough. This paper also proposes a method for estimation of the probability of an aggregated classification. The estimated probability of the aggregated classification is essential if the performance of the ensemble of classifiers needs to be expressed in terms of Area Under the Receiver Operating Curve or some other performance measures that classifications’ probability. The experimental results demonstrate the performance improvements obtained by applying the proposed methods to an ensemble of classifiers compared to individual classifiers.
Publisher: IASTED, Acta Press
Relation: Proceedings of Artificial Intelligence and Applications 2011
Identifier: oai:repository.ukim.mk:20.500.12188/21234
Identifier: http://hdl.handle.net/20.500.12188/21234
