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Improving the accuracy of classification algorithms for inductive learning rules using wrapper methods
In this paper we investigate the problem of the accuracy of classifier using wrapper methods. For the purposes of classification is used a large number of algorithms: IBK, Naïve Bayes, SVM, J48 decision tree and RBF networks. Experimental results show that wrapper methods can rapidly identify irrelevant, redundant attributes, as well as the noise in the data, if any; and those attributes which are important for the studied phenomenon. The paper prove that applying wrapper methods for reducing the dimensionality of the data it is possible to significantly improve system performance for inductive learning rules in classification problems.
Improving the accuracy of classification algorithms for inductive learning rules using wrapper methods
In this paper we investigate the problem of the accuracy of classifier using wrapper methods. For the purposes of classification is used a large number of algorithms: IBK, Naïve Bayes, SVM, J48 decision tree and RBF networks. Experimental results show that wrapper methods can rapidly identify irrelevant, redundant attributes, as well as the noise in the data, if any; and those attributes which are important for the studied phenomenon. The paper prove that applying wrapper methods for reducing the dimensionality of the data it is possible to significantly improve system performance for inductive learning rules in classification problems.
Improving the accuracy of classification algorithms for inductive learning rules using wrapper methods
Novaković Jasmina Đ. (author)
2015
Article (Journal)
Electronic Resource
Unknown
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