@article{(Open Science Index):https://publications.waset.org/pdf/2571,
	  title     = {Binary Classification Tree with Tuned Observation-based Clustering},
	  author    = {Maythapolnun Athimethphat and  Boontarika Lerteerawong},
	  country	= {},
	  institution	= {},
	  abstract     = {There are several approaches for handling multiclass classification. Aside from one-against-one (OAO) and one-against-all (OAA), hierarchical classification technique is also commonly used. A binary classification tree is a hierarchical classification structure that breaks down a k-class problem into binary sub-problems, each solved by a binary classifier. In each node, a set of classes is divided into two subsets. A good class partition should be able to group similar classes together. Many algorithms measure similarity in term of distance between class centroids. Classes are grouped together by a clustering algorithm when distances between their centroids are small. In this paper, we present a binary classification tree with tuned observation-based clustering (BCT-TOB) that finds a class partition by performing clustering on observations instead of class centroids. A merging step is introduced to merge any insignificant class split. The experiment shows that performance of BCT-TOB is comparable to other algorithms.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {4},
	  year      = {2012},
	  pages     = {455 - 460},
	  ee        = {https://publications.waset.org/pdf/2571},
	  url   	= {https://publications.waset.org/vol/64},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 64, 2012},
	}