Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms
In this study, a comprehensive methodology for overcoming the design problem of the Fuzzy ARTMAP neural network is proposed. The issues addressed are the sequence of training data for supervised learning and optimum parameter tuning for parameters such as baseline vigilance. A genetic algorithm sear...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer Verlag (Germany)
2015
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/13734/1/Probabilistic_ensemble_Fuzzy_ARTMAP_optimization.pdf |
| _version_ | 1825720405981134848 |
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| author | Loo, C.K. Liew, W.S. Seera, M. Lim, Einly |
| author_facet | Loo, C.K. Liew, W.S. Seera, M. Lim, Einly |
| author_sort | Loo, C.K. |
| collection | UM |
| description | In this study, a comprehensive methodology for overcoming the design problem of the Fuzzy ARTMAP neural network is proposed. The issues addressed are the sequence of training data for supervised learning and optimum parameter tuning for parameters such as baseline vigilance. A genetic algorithm search heuristic was chosen to solve this multi-objective optimization problem. To further augment the ARTMAP's pattern classification ability, multiple ARTMAPs were optimized via genetic algorithm and assembled into a classifier ensemble. An optimal ensemble was realized by the inter-classifier diversity of its constituents. This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. The best-performing classifiers were then combined in an ensemble, using probabilistic voting for decision combination. This study also integrated the disparate methods to operate within a single framework, which is the proposed novel method for creating an optimum classifier ensemble configuration with minimum user intervention. The methodology was benchmarked using popular data sets from UCI machine learning repository. |
| first_indexed | 2024-03-06T05:34:39Z |
| format | Article |
| id | um.eprints-13734 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:34:39Z |
| publishDate | 2015 |
| publisher | Springer Verlag (Germany) |
| record_format | dspace |
| spelling | um.eprints-137342020-01-16T01:53:45Z http://eprints.um.edu.my/13734/ Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms Loo, C.K. Liew, W.S. Seera, M. Lim, Einly T Technology (General) TA Engineering (General). Civil engineering (General) In this study, a comprehensive methodology for overcoming the design problem of the Fuzzy ARTMAP neural network is proposed. The issues addressed are the sequence of training data for supervised learning and optimum parameter tuning for parameters such as baseline vigilance. A genetic algorithm search heuristic was chosen to solve this multi-objective optimization problem. To further augment the ARTMAP's pattern classification ability, multiple ARTMAPs were optimized via genetic algorithm and assembled into a classifier ensemble. An optimal ensemble was realized by the inter-classifier diversity of its constituents. This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. The best-performing classifiers were then combined in an ensemble, using probabilistic voting for decision combination. This study also integrated the disparate methods to operate within a single framework, which is the proposed novel method for creating an optimum classifier ensemble configuration with minimum user intervention. The methodology was benchmarked using popular data sets from UCI machine learning repository. Springer Verlag (Germany) 2015-02 Article PeerReviewed application/pdf en http://eprints.um.edu.my/13734/1/Probabilistic_ensemble_Fuzzy_ARTMAP_optimization.pdf Loo, C.K. and Liew, W.S. and Seera, M. and Lim, Einly (2015) Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms. Neural Computing and Applications, 26 (2). pp. 263-276. ISSN 0941-0643, DOI https://doi.org/10.1007/s00521-014-1632-y <https://doi.org/10.1007/s00521-014-1632-y>. http://link.springer.com/article/10.1007/s00521-014-1632-y DOI 10.1007/s00521-014-1632-y |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) Loo, C.K. Liew, W.S. Seera, M. Lim, Einly Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms |
| title | Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms |
| title_full | Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms |
| title_fullStr | Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms |
| title_full_unstemmed | Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms |
| title_short | Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms |
| title_sort | probabilistic ensemble fuzzy artmap optimization using hierarchical parallel genetic algorithms |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://eprints.um.edu.my/13734/1/Probabilistic_ensemble_Fuzzy_ARTMAP_optimization.pdf |
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