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Hybrid coevolutionary algorithms vs. SVM algorithms

Published: 07 July 2007 Publication History

Abstract

As a learning method support vector machine is regarded as one of the best classifiers with a strong mathematical foundation. On the other hand, evolutionary computational technique is characterized as a soft computing learning method with its roots in the theory of evolution. During the past decade, SVM has been commonly used as a classifier for various applications. The evolutionary computation has also attracted a lot of attention in pattern recognition and has shown significant performance improvement on a variety of applications. However, there has been no comparison of the two methods. In this paper, first we propose an improvement of a coevolutionary computational classification algorithm, called Improved Coevolutionary Feature Synthesized EM (I-CFS-EM) algorithm. It is a hybrid of coevolutionary genetic programming and EM algorithm applied on partially labeled data. It requires less labeled data and it makes the test in a lower dimension, which speeds up the testing. Then, we provide a comprehensive comparison between SVM with different kernel functions and I-CFS-EM on several real datasets. This comparison shows that I-CFS-EM outperforms SVM in the sense of both the classification performance and the computational efficiency in the testing phase. We also give an intensive analysis of the pros and cons of both approaches.

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  • (2019)Selecting training sets for support vector machinesArtificial Intelligence Review10.1007/s10462-017-9611-152:2(857-900)Online publication date: 31-Jul-2019
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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 July 2007

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Author Tags

  1. co-evolution
  2. machine learning
  3. pattern classification
  4. support vector machines

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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View all
  • (2022)Reduction of training data for support vector machine: a surveySoft Computing10.1007/s00500-022-06787-526:8(3729-3742)Online publication date: 16-Mar-2022
  • (2021)LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classificationBMC Bioinformatics10.1186/s12859-021-04485-x22:1Online publication date: 26-Nov-2021
  • (2019)Selecting training sets for support vector machinesArtificial Intelligence Review10.1007/s10462-017-9611-152:2(857-900)Online publication date: 31-Jul-2019
  • (2018)Content-based image retrieval and semantic automatic image annotation based on the weighted average of triangular histograms using support vector machineApplied Intelligence10.1007/s10489-017-0957-548:1(166-181)Online publication date: 1-Jan-2018

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