Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/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, Electrical, Automation, Control and Information Engineering},  volume    = {6},
  number    = {4},
  year      = {2012},
  pages     = {455 - 460},
  ee        = {http://waset.org/publications/2571},
  url       = {http://waset.org/Publications?p=64},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 64, 2012},
}