An Extension to the Composite Rule Induction System
Discovering knowledge from data is an important task for knowledge management and development of intelligent systems, which is called knowledge acquisition or data mining. Many techniques have been developed for such purpose. For example, ID3, C4.5 (tree induction techniques) and Artificial Neural Networks are among the popular techniques in ¡§Classification and Prediction¡¨ area. However, these methods often use the same criteria to analyze nominal and non-nominal attributes, which is very likely to produce biased knowledge due to mis-match between data type and their algorithms.
In Liang (1992), he proposed a composite approach called CRIS to inducing knowledge that introduces statistical concepts and data mining heuristics and found the composite method outperformed other methods including tree induction, discriminant analysis, and neural networks. However, the paper focuses on the classification of binary objects and did not describe how the approach can be applied to a problem with more than two classes in the dependent variable.
In this research, we extend the previous approach to solve the problem with more than two classes. We also enhance the approach by adding steps to prioritizing attributes using their identification power and controlling the growth of generated hypothesis. In order evaluate the extended CRIS method, a prototype system, eCRIS, was developed and compared with a commercial data mining package, XLMiner3 (developed by Cytel Software Corporation) using three existing datasets in data mining research. The results indicate that the extended CRIS outperforms tree induction and backpropagation in neural networks in datasets that include both nominal and non-nominal data and performed equally well with them.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0730107-180923 |
| Date | 30 July 2007 |
| Creators | Yang, Yuan-chi |
| Contributors | Ting-Peng Liang, Bing-Chiang Jeng, Deng-Neng Chen |
| 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-0730107-180923 |
| Rights | withheld, Copyright information available at source archive |
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