It has been shown that focusing the training algorithms to the decision boundary vicinity data can improve the accuracy of several classification methods. However, previous approaches for fining decision boundary vicinity data are either computationally tedious or may perform poorly in handling problems with class overlapping. With the application of the nearest neighbor rule, this work proposes a new criterion to characterize the nearness of the training samples to the decision boundary. To demonstrate the effectiveness of the proposed approach, the proposed method is integrated with a nearest neighbor classifier design method and a neural work training approach. Experimental results show that the proposed method can reduce the size and classification error for both of the tested classifiers.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0726101-051513 |
| Date | 26 July 2001 |
| Creators | Young, Chieh-Neng |
| Contributors | Chi-Cheng Cheng, Innchyn Her, Chen-Wen Yen |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726101-051513 |
| Rights | unrestricted, Copyright information available at source archive |
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