IJBTT

Enhanced Classification to Counter the Problem of Cluster Disjuncts

	International Journal of Computer Trends and Technology (IJCTT)
	© 2014 by IJCTT Journal
	Volume-18 Number-5
	Year of Publication : 2014
	Authors : Syed Ziaur Rahman , Dr.G. Samuel Vara Prasad Raju
	DOI :  10.14445/22312803/IJCTT-V18P148

Syed Ziaur Rahman , Dr.G. Samuel Vara Prasad Raju "Enhanced Classification to Counter the Problem of Cluster Disjuncts". International Journal of Computer Trends and Technology (IJCTT) V18(5):217-224, Dec 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
This paper presets a rigorous yet practical model dubbed as Cluster Disjunct Minority Oversampling Technique (CDMOTE) for learning from skewed training data. This algorithm provides a simpler and faster alternative by using cluster disjunct concept. We conduct experiments using fifteen UCI data sets from various application domains using five algorithms for comparison on six evaluation metrics. The empirical study suggests that CDMOTE have been believed to be effective in addressing the class imbalance problem.

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Keywords
Classification, class imbalance, cluster disjunct, CDMOTE.