@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}, }