## A Comparative Study of Discretization Methods for Naive-Bayes Classifiers (2002)

Venue: | In Proceedings of PKAW 2002: The 2002 Pacific Rim Knowledge Acquisition Workshop |

Citations: | 18 - 0 self |

### BibTeX

@INPROCEEDINGS{Yang02acomparative,

author = {Ying Yang and Geoffrey I. Webb},

title = {A Comparative Study of Discretization Methods for Naive-Bayes Classifiers},

booktitle = {In Proceedings of PKAW 2002: The 2002 Pacific Rim Knowledge Acquisition Workshop},

year = {2002},

pages = {159--173}

}

### OpenURL

### Abstract

Discretization is a popular approach to handling numeric attributes in machine learning. We argue that the requirements for effective discretization differ between naive-Bayes learning and many other learning algorithms. We evaluate the effectiveness with naive-Bayes classifiers of nine discretization methods, equal width discretization (EWD), equal frequency discretization (EFD), fuzzy discretization (FD), entropy minimization discretization (EMD), iterative discretization (ID), proportional k-interval discretization (PKID), lazy discretization (LD), nondisjoint discretization (NDD) and weighted proportional k-interval discretization (WPKID). It is found that in general naive-Bayes classifiers trained on data preprocessed by LD, NDD or WPKID achieve lower classification error than those trained on data preprocessed by the other discretization methods. But LD can not scale to large data. This study leads to a new discretization method, weighted non-disjoint discretization (WNDD) that combines WPKID and NDD's advantages. Our experiments show that among all the rival discretization methods, WNDD best helps naive-Bayes classifiers reduce average classification error.

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Citation Context ...timation. For simplicity, we take k = 3 for demonstration.sAtomic Interval Interval Fig. 1. Atomic Intervals Compose Actual Intervals 4.9 Weighted Proportional k-Interval Discretization (WPKID) WPKID =-=[35]-=- is an improved version of PKID. It is credible that for smaller datasets, variance reduction can contribute more to lower naive-Bayes learning error than bias reduction [12]. Thus fewer intervals eac... |

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Citation Context ...th high dimensional attribute spaces and huge numbers of instances are increasingly used in real-world applications, a study of these methods’ performance on large datasets is necessary and desirabl=-=e [11, 27]-=-. Nine discretization methods are included in this comparative study, each of which is either designed especially for naive-Bayes classifiers or is in practice often used for naive-Bayes classifiers. ... |

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Citation Context ...space of Xi, for any particular xi ∈ Si, the probability p(Xi = xi) will be arbitrarily close to 0. The probability distribution of Xi is completely determined by a density function f which satisfie=-=s [30]: 1. f(xi) ≥ 0-=-, ∀xi ∈ Si; 2. � Si f(Xi)dXi = 1; 3. � bi ai f(Xi)dXi = p(ai < Xi ≤ bi), ∀(ai, bi] ∈ Si. p(Xi = xi | C = c) can be estimated from f [17]. But for real-world data, f is usually unknown. U... |