Improvement of Accuracy of Decision Tree By Reprocessing 


Vol. 10,  No. 6, pp. 593-598, Oct.  2003
10.3745/KIPSTB.2003.10.6.593


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  Abstract

Machine learning organizes knowledge for efficient and accurate reuse. This paper is concerned with methods of concept learning from examples, which glean knowledge from a training set of preclassified ´objects´. Ideally, training facilitates classification of novel, previously unseen objects. However, every learning system relies on processing and representation assumptions that may be detrimental under certain circumstances. We explore the biases of a well-known learning system, ID3, review improvements, and introduce some improvements of our own, each designed to yield accurate and pedagogically sound classification.

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[IEEE Style]

L. G. Seong, "Improvement of Accuracy of Decision Tree By Reprocessing," The KIPS Transactions:PartB , vol. 10, no. 6, pp. 593-598, 2003. DOI: 10.3745/KIPSTB.2003.10.6.593.

[ACM Style]

Lee Gye Seong. 2003. Improvement of Accuracy of Decision Tree By Reprocessing. The KIPS Transactions:PartB , 10, 6, (2003), 593-598. DOI: 10.3745/KIPSTB.2003.10.6.593.

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