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Artificial bee colony-based extraction of non-taxonomic relation between symptom and syndrome in TCM records

Authors:

Shouqiang Chen,

Liang XuAuthors Info & Claims

International Journal of Computing Science and Mathematics, Volume 6, Issue 6

Pages 600 - 610

https://doi.org/10.1504/IJCSM.2015.073600

Published: 01 December 2015 Publication History

Abstract

Research the extraction of non-taxonomic relation between symptom and syndrome in Traditional Chinese Medicine TCM records. In the present study, a niche-based artificial bee colony ABC algorithm is proposed to address a variety of problems e.g., low efficiency, slow convergence rate, miss-reporting and etc. of conventional rule-oriented extraction of non-taxonomic relation. By combing the vast merging/evolution diversity of niche technique and rapid non-taxonomic relation extraction of ABC algorithm, the proposed algorithm was able to resolve the problems of local optimum and rules redundancy. In addition, confirmatory experiment is performed on TCM medical record corpus; the results show that, when compared with the conventional relationship rule mining algorithm, the novel algorithm featured significant improvements in both the individual diversity and the efficiency of extracting effective rules, and thus excavating the results which can be used as references for extraction of non-taxonomic relation between symptom and syndrome in TCM medical records.

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Cited By

(2018)A self-adaptive particle swarm optimisation and bacterial foraging hybrid algorithmInternational Journal of Wireless and Mobile Computing10.5555/3052739.305275011:3(258-265)Online publication date: 21-Dec-2018
https://dl.acm.org/doi/10.5555/3052739.3052750
Wang W(2018)A new artificial bee colony based on GPU for solving large-scale production scheduling problemInternational Journal of Wireless and Mobile Computing10.1504/IJWMC.2016.07820310:4(393-398)Online publication date: 21-Dec-2018
https://dl.acm.org/doi/10.1504/IJWMC.2016.078203
(2018)Comparative analysis of selection schemes used in artificial bee colony algorithmInternational Journal of Computing Science and Mathematics10.1504/IJCSM.2017.0857398:3(218-227)Online publication date: 16-Dec-2018
https://dl.acm.org/doi/10.1504/IJCSM.2017.085739
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Published In

cover image International Journal of Computing Science and Mathematics

International Journal of Computing Science and Mathematics Volume 6, Issue 6

December 2015

101 pages

ISSN:1752-5055

EISSN:1752-5063

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Inderscience Publishers

Geneva 15, Switzerland

Publication History

Published: 01 December 2015

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Cited By

(2018)A self-adaptive particle swarm optimisation and bacterial foraging hybrid algorithmInternational Journal of Wireless and Mobile Computing10.5555/3052739.305275011:3(258-265)Online publication date: 21-Dec-2018
https://dl.acm.org/doi/10.5555/3052739.3052750
Wang W(2018)A new artificial bee colony based on GPU for solving large-scale production scheduling problemInternational Journal of Wireless and Mobile Computing10.1504/IJWMC.2016.07820310:4(393-398)Online publication date: 21-Dec-2018
https://dl.acm.org/doi/10.1504/IJWMC.2016.078203
(2018)Comparative analysis of selection schemes used in artificial bee colony algorithmInternational Journal of Computing Science and Mathematics10.1504/IJCSM.2017.0857398:3(218-227)Online publication date: 16-Dec-2018
https://dl.acm.org/doi/10.1504/IJCSM.2017.085739
(2018)A framework for generating prioritised test scenarios using firefly optimisation techniqueInternational Journal of Computing Science and Mathematics10.1504/IJCSM.2017.0857248:3(228-237)Online publication date: 16-Dec-2018
https://dl.acm.org/doi/10.1504/IJCSM.2017.085724

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