FEATURE SELECTION FOR THE FUZZY ARTMAP NEURAL NETWORK USING A HYBRID GENETIC ALGORITHM AND TABU SEARCH
| dc.contributor.author | TANG, WENG CHIN | |
| dc.date.accessioned | 2016-01-12T03:25:06Z | |
| dc.date.available | 2016-01-12T03:25:06Z | |
| dc.date.issued | 2007-07 | |
| dc.description.abstract | The performance of Neural-Network (NN)-based classifiers is strongly dependent on the data set used for learning. In practice, a data set may contain noisy or redundant data items. Thus, feature selection is an important step in building an effective and efficient NN-based classifier. In this thesis, the research of a hybrid algorithm of Genetic Algorithm (GA) and Tabu Search (TS) for feature selection in the Fuzzy ARTMAP NN classifier is presented. The proposed GA-TS algorithm embeds the recency and frequency memory structures of TS into the search process of the GA. The recency memory structure helps induce an additional diversification mechanism in the GA search process. On the other hand, the frequency memory structure provides guidance to genetic operator and helps intensify the GA search process. A series of empirical studies comprising benchmark and real-world problems is employed to evaluate the effectiveness of the proposed hybrid GA-TS algorithm. A simulated noisy feature injection method is devised to assess the capabilities of GA-TS in identifying and removing noisy features that can degrade classification accuracy. Experimental results demonstrate that proposed GA-TS performs better in terms of feature compactness (the number of features reduced) and classification accuracy than the ordinary GA. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/1363 | |
| dc.subject | NETWORK | en_US |
| dc.subject | SEARCH | en_US |
| dc.title | FEATURE SELECTION FOR THE FUZZY ARTMAP NEURAL NETWORK USING A HYBRID GENETIC ALGORITHM AND TABU SEARCH | en_US |
| dc.type | Thesis | en_US |
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