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Efficient Associative Classification using Genetic Network Programming
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ibrahim, S. P. Syed |
| Copyright Year | 2011 |
| Abstract | Classification and association rule mining are the two important tasks addressed in the data mining literature. Associative classification method applies association rule mining technique in classification and achieves higher classification accuracy. Associative classification method typically yields a large number of rules, from which a set of high quality rules are chosen to construct an efficient classifier. Hence generating a small subset of high-quality rules without jeopardizing the classification accuracy is of prime importance but indeed a challenging task. This paper proposes an efficient information gain based associative classification method using genetic network programming, which generates sufficient number of rules to construct the accurate classifier. Experimental results show that, the proposed method outperforms the existing genetic network based associative classification method and traditional decision tree classification algorithm. General Terms Data Mining, Information Extraction |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://research.ijcaonline.org/volume29/number6/pxc3874929.pdf |
| Alternate Webpage(s) | http://www.ijcaonline.org/archives/volume29/number6/3572-4929?format=pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Association rule learning Computer network programming Data mining Decision tree Display resolution Gene regulatory network Information extraction Information gain in decision trees Kullback–Leibler divergence Rule (guideline) Subgroup |
| Content Type | Text |
| Resource Type | Article |
