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Support Vector Machine Optimized by Elephant Herding Algorithm for Erythemato-Squamous Diseases Detection
Author(s) -
Eva Tuba,
Ivana Ribic,
Romana Capor Hrosik,
Milan Tuba
Publication year - 2017
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2017.11.455
Subject(s) - computer science , herding , support vector machine , algorithm , artificial intelligence , field (mathematics) , speedup , machine learning , swarm intelligence , process (computing) , pattern recognition (psychology) , data mining , particle swarm optimization , parallel computing , mathematics , pure mathematics , forestry , geography , operating system
Machine learning algorithms are used in numerous field and medicine is one of them. Automatic diagnosis or detection of different diseases based on list of symptoms can drastically improve and speedup diagnostics process. Determining diagnosis at earlier stages gives better healing results. In this paper a method for automatic erythemato-squamous diseases classification was proposed. Six erythemato-squamous diseases that are very hard to distinguish were classified by the optimized support vector machine. Recent swarm intelligent algorithm, elephant herding optimization algorithm was used to find optimal parameters for the support vector machine that was then used to determine the exact erythemato-squamous diseases. We compared accuracy of our proposed method to other approaches from literature using standard dataset and it obtained better results in all experiments.

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