A Hybrid Method for Fast Finding the Reduct with the Best Classification Accuracy
| Autor: | Şirzat Kahramanli, Mehmet Hacibeyoglu, Ahmet Arslan |
|---|---|
| Přispěvatelé: | Selçuk Üniversitesi |
| Rok vydání: | 2013 |
| Předmět: |
Reduct
lcsh:Computer engineering. Computer hardware decision trees General Computer Science business.industry Decision tree lcsh:TK7885-7895 Pattern recognition Feature selection artificial intelligence Machine learning computer.software_genre Statistical classification feature selection ComputingMethodologies_PATTERNRECOGNITION discernibility function classification algorithms lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence Electrical and Electronic Engineering business lcsh:TK1-9971 computer Mathematics |
| Zdroj: | Advances in Electrical and Computer Engineering, Vol 13, Iss 4, Pp 57-64 (2013) |
| ISSN: | 1844-7600 1582-7445 0003-3146 |
| DOI: | 10.4316/aece.2013.04010 |
| Popis: | WOS: 000331461300010 Usually a dataset has a lot of reducts finding all of which is known to be an NP hard problem. On the other hand, different reducts of a dataset may provide different classification accuracies. Usually, for every dataset, there is only a reduct with the best classification accuracy to obtain this best one, firstly we obtain the group of attributes that are dominant for the given dataset by using the decision tree algorithm. Secondly we complete this group up to reducts by using discernibility function techniques. Finally, we select only one reduct with the best classification accuracy by using data mining classification algorithms. The experimental results for datasets indicate that the classification accuracy is improved by removing the irrelevant features and using the simplified attribute set which is derived from proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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