CloNI: Clustering Of Square Root Of N -interval Discretization
Author(s)
C. Ratanamahatana
Abstract
CloNI: clustering of JN -interval discretization
C. Ratanamahatana
Department of Computer Science, University of California,
Riverside, USA
Abstract
It is known that the naive Bayesian classifier typically works well on discrete
data. All continuous attributes then need to be discretized beforehand for such
applications. An inappropriate range of discretization intervals may result in
degradation of performance. In this paper, we review previous work on
continuous feature discretization and conduct an empirical evaluation of an
improved method called Clustering of &-Interval Discretization (CloNI).
CloNI tries to reduce the number of fi intervals in the datasets by iteratively
combining two consecutive intervals together, according to their median distance
until a stopping criteria is met. We also show that even though C4.5 decision
trees can handle continuous features, we can significantly improve its
performance in some domains if those features were discretized in advance. In
our empirical
Keywords
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