Multivariate Interdependent Discretization In
Discovering The Best Correlated Attribute
Author(s)
S. Chao & Y. P. Li
Abstract
The decision tree is one of the most widely used and practical methods in data
mining. However, many discretization algorithms developed in this field focus
on the univariate only, which discretize continuous-valued attributes
independently, without considering the interdependent relationship between
other attributes, at most taking the class attribute into account. Such univariate
discretization is inadequate to handle the critical problems especially owned in
the medical domain. In this paper, we propose a new multivariate discretization
method called Multivariate Interdependent Discretization for Continuous
Attributes – MIDCA. This method incorporates the normalized relief and
information measures to look for the best correlated attribute with respect to each
continuous-valued attribute being discretized, and using the discovered best
correlated attribute as the interdependent attribute to carry out the multivariate
discretization. We believe that a good multivariate discretization scheme for
continuous-valued attributes should rely highly on their perfect correlated
attributes respectively. Among an attribute space, each attribute should have at
least one most relevant attribute that may be different from others. Our novel
multivariate discretization algorithm can minimize the uncertainty between the
interdependent attribute and the continuous-valued attribute being discretized
and at the same time maximize their correlation. Such a method can be used as a
pre-processing step for the learning algorithms. The empirical results
demonstrate a comparison of performance between MIDCA and various
discretization methods for two decision tree algorithms ID3 and C4.5 on twelve
real-life datasets from UCI repository.
Keywords: multivariate discretization, interdependent feature, correlated
attribute, data mining, machine learning.
Keywords
multivariate discretization, interdependent feature, correlated
attribute, data mining, machine learning.
Related Book
Other papers in this volume
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