Optimized classification predictions with a new index combining machine learning algorithms

Tamvakis, A., Anagnostopoulos, C.-N., Tsirtsis, G., Niros, A. D. and Spatharis, S. (2018) Optimized classification predictions with a new index combining machine learning algorithms. International Journal on Artificial Intelligence Tools, 27(3), 1850012. (doi: 10.1142/s0218213018500124)

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Abstract

Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Spatharis, Dr Sofie
Authors: Tamvakis, A., Anagnostopoulos, C.-N., Tsirtsis, G., Niros, A. D., and Spatharis, S.
College/School:College of Medical Veterinary and Life Sciences > School of Life Sciences
Journal Name:International Journal on Artificial Intelligence Tools
Publisher:World Scientific Publishing
ISSN:0218-2130
ISSN (Online):1793-6349
Published Online:21 May 2018
Copyright Holders:Copyright © 2018 World Scientific Publishing Co Pte Ltd
First Published:First published in International Journal on Artificial Intelligence Tools 27(3): 1850012
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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