CloNI: clustering of JN -interval discretization
Chotirat Ann Ratanamahatana · 2003
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 results, using discretized instead of continuous features in C4.5 never significantly degrades its accuracy. Our results indicate that CloNI reliably performs as well as or better than the Proportional k-interval Discretization (PKID) on all domains, and gives a competitive classification performance for both smaller and larger datasets.