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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163013.pdf - Accepted Version 1MB |
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 |
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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 College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine |
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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