Hybrid models of fuzzy artmap and qlearning for pattern classification
| dc.contributor.author | Farhad Pourpanah Navan | |
| dc.date.accessioned | 2021-05-03T03:27:27Z | |
| dc.date.available | 2021-05-03T03:27:27Z | |
| dc.date.issued | 2015-10-01 | |
| dc.description.abstract | Pattern classification is one of the primary issues in various data mining tasks. In this study, the main research focus is on the design and development of hybrid models, combining the supervised Adaptive Resonance Theory (ART) neural network and Reinforcement Learning (RL) models for pattern classification. Specifically, the Fuzzy ARTMAP (FAM) network and Q-learning are adopted as the backbone for designing and developing the hybrid models. A new QFAM model is first introduced to improve the classification performance of FAM network. A pruning strategy is incorporated to reduce the complexity of QFAM. To overcome the opaqueness issue, a Genetic Algorithm (GA) is used to extract fuzzy if-then rules from QFAM. The resulting model, i.e. QFAM-GA, is able to provide predictions with explanations using only a few antecedents. To further improve the robustness of QFAM-based models, the notion of multi agent systems is employed. As a result, an agent-based QFAM ensemble model with a new trust measurement and negotiation method is proposed. A variety of benchmark problems are used for evaluation of individual and ensemble QFAM-based models. The results are analyzed and compared with those from FAM as well as other models reported in the literature. In addition, two real-world problems are used to demonstrate the practicality of the hybrid models. The outcomes indicate the effectiveness of QFAM-based models in tackling pattern classification tasks. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/13220 | |
| dc.language.iso | en | en_US |
| dc.title | Hybrid models of fuzzy artmap and qlearning for pattern classification | en_US |
| dc.type | Thesis | en_US |
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