A hybrid model of fuzzy artmap and the genetic algorithm for data classification

Manjeevan Seera, Wei Shiung Liew, Chu Kiong Loo

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review


A framework for optimizing Fuzzy ARTMAP (FAM) neural networks using Genetic Algorithms (GAs) is proposed in this paper. A number of variables were identified for optimization, which include the presentation order of training data during the learning step, the feature subset selection of the training data, and the internal parameters of the FAM such as baseline vigilance and match tracking. A single configuration of all three variables were encoded as a chromosome string and evaluated by creating and training the FAM according to the variables. The fitness of the chromosome is determined by the final classification accuracy of the FAM. Evaluation on benchmark data sets are conducted with the results compared with literature. Experimental results indicate the effectiveness of the proposed framework in undertaking data classification tasks.

Original languageEnglish
Title of host publicationNeural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings
EditorsWeng Kin Lai, Qingshan Liu, Tingwen Huang, Sabri Arik
Number of pages8
ISBN (Print)9783319265346
Publication statusPublished - 2015
Externally publishedYes
EventInternational Conference on Neural Information Processing 2015 - Istanbul, Türkiye
Duration: 9 Nov 201512 Nov 2015
Conference number: 22nd

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Neural Information Processing 2015
Abbreviated titleICONIP 2015
Internet address

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