Genetic Algorithm Based Fuzzy ID3 Method and Its Pruning Study
| Autor: | CHAO-MING CHANG, 張昭銘 |
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| Rok vydání: | 2006 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 94 ID3 algorithm is a popular and efficient method for decision tree induction from symbolic data. However, most knowledge associated with human’s thinking and perception has some imprecision and uncertainty. For the purpose of handling imprecise and uncertain knowledge; hence, ID3 has been expanded to developed a kind of decision tree is fuzzy ID3 algorithm, which is similar to ID3 algorithm and is extended to apply a data set containing continuous attribute values. But fuzzy ID3 algorithm can only deal with continuous data and it is often criticized to result in poor learning accuracy. In this thesis, we propose a genetic algorithm based fuzzy ID3 method to construct fuzzy classification system, which can accept continuous, discrete, or mixed-mode data sets. Furthermore, we formulate and compare three pruning methods, then choose better pruning method of decision tree to obtain better accuracy or a more efficient rule base. We have tested our method on some famous data sets, and the results of a five-fold cross validation are compared to those by C5.0. The experiments show that our method works better in practice. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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