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Bayes Classifier (Baye + classifier)

Distribution by Scientific Domains
Information Science and Computing75%

Selected Abstracts

Glass analysis for forensic purposes,a comparison of classification methods

JOURNAL OF CHEMOMETRICS, Issue 5-6 2007
Grzegorz Zadora
Abstract One of the purposes of the chemical analysis of glass fragments (pieces of glass of linear dimension ca. 0.5,mm) for forensic purposes is a classification of those fragments into use categories, for example windows, car headlights and containers. The object of this research was to check the efficiency of Naïve Bayes Classifiers (NBCs) and Support Vector Machines (SVMs) to the application of the classification of glass objects when those objects may be described by the major and minor elemental concentrations obtained by Scanning Electron Microscopy coupled with an Energy Dispersive X-ray spectrometer which is routinely used in many forensic institutes. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Predicting project delivery rates using the Naive,Bayes classifier

JOURNAL OF SOFTWARE MAINTENANCE AND EVOLUTION: RESEARCH AND PRACTICE, Issue 3 2002
B. Stewart
Abstract The importance of accurate estimation of software development effort is well recognized in software engineering. In recent years, machine learning approaches have been studied as possible alternatives to more traditional software cost estimation methods. The objective of this paper is to investigate the utility of the machine learning algorithm known as the Naive,Bayes classifier for estimating software project effort. We present empirical experiments with the Benchmark 6 data set from the International Software Benchmarking Standards Group to estimate project delivery rates and compare the performance of the Naive,Bayes approach to two other machine learning methods,model trees and neural networks. A project delivery rate is defined as the number of effort hours per function point. The approach described is general and can be used to analyse not only software development data but also data on software maintenance and other types of software engineering. The paper demonstrates that the Naive,Bayes classifier has a potential to be used as an alternative machine learning tool for software development effort estimation. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Effective database processing for classification and regression with continuous variables

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2007
E. Di Tomaso
This article proposes a method for manipulating a database of instances relative to discrete and continuous variables. A fuzzy partition is used to discretize continuous domains. A reorganized form of representing a relational database is proposed. The new form of representation is called an effective database. The effective database is tested on classification and regression problems using general Bayesian networks and Näive Bayes classifiers. The structures and the parameters of the classifiers are estimated from the effective database. An algorithm for updating with soft evidence is used to test the induced models, when continuous variables are present. The experiments show that the effective database procedure produces a selection of relevant information from data, which improves in some cases the prediction accuracy of the classifiers. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1271,1285, 2007. [source]