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2010•Computer Engineering and Applications JournalRequires access

Discretization of continuous attributes using information divergence

Deqin Yan

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Abstract

The discretization of continuous attributes is always with great contribution to the followed process of machine learning or data mining.A new algorithm based on information divergence for discretization is proposed.By an inconsistency checking,the procedure of discretization is controlled.The experiments are performed respectively with the results of discreted data by using C4.5 and SVM.The results show that the presented algorithm is effective.

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What this paper is about

The discretization of continuous attributes is always with great contribution to the followed process of machine learning or data mining.A new algorithm based on information divergence for discretization is proposed.By an inconsistency checking,the procedure of discretization is controlled.The experiments are performed respectively with the results of discreted data by using C4.5 and SVM.The results show that the presented algorithm is effective.

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Available abstract

The discretization of continuous attributes is always with great contribution to the followed process of machine learning or data mining.A new algorithm based on information divergence for discretization is proposed.By an inconsistency checking,the procedure of discretization is controlled.The experiments are performed respectively with the results of discreted data by using C4.5 and SVM.The results show that the presented algorithm is effective.

Key concepts: Discretization, Discretization of continuous features, Divergence (linguistics), Process (computing), Computer science, Support vector machine, Discretization error, Mathematical optimization

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