id: 05757430
dt: a
an: 05757430
au: Haraguchi, Kazuya; Hong, Seok-Hee; Nagamochi, Hiroshi
ti: Multiclass visual classifier based on bipartite graph representation of
    decision tables.
so: Blum, Christian (ed.) et al., Learning and intelligent optimization. 4th
    international conference, LION 4, Venice, Italy, January 18‒22, 2010.
    Selected papers. Berlin: Springer (ISBN 978-3-642-13799-0/pbk). Lecture
    Notes in Computer Science 6073, 169-183 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-13800-3_13
ab: Summary: In this paper, we consider K-class classification problem, a
    significant issue in machine learning or artificial intelligence. In
    this problem, we are given a training set of samples, where each sample
    is represented by a nominal-valued vector and is labeled as one of the
    predefined $K$ classes. The problem asks to construct a classifier that
    predicts the classes of future samples with high accuracy. For $K = 2$,
    we have studied a new visual classifier named 2-class SE-graph based
    classifier (2-SEC) in our previous works, which is constructed as
    follows: We first create several decision tables from the training set
    and extract a bipartite graph called an SE-graph that represents the
    relationship between the training set and the decision tables. We draw
    the SE-graph as a two-layered drawing by using an edge crossing
    minimization technique, and the resulting drawing acts as a visual
    classifier. We can extend 2-SEC to $K$-SEC for $K > 2$ naturally, but
    this extension does not consider the relationship between classes, and
    thus may perform badly on some data sets. In this paper, we propose
    SEC-TREE classifier for $K > 2$, which decomposes the given $K$-class
    problem into subproblems for fewer classes. Following our philosophy,
    we employ edge crossing minimization technique for this decomposition.
    Compared to previous decomposition strategies, SEC-TREE can extract any
    tree as the subproblem hierarchy. In computational studies, SEC-TREE
    outperforms C4.5 and is competitive with SVM especially when $K$ is
    large.
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