Partial histogram bayes learning algorithm for classification applications

Haider O. Lawend, Anuar Mikdad Muad, Aini Hussain

Research output: Contribution to journalArticle

Abstract

This paper presents a proposed supervised classification technique namely partial histogram Bayes (PHBayes) learning algorithm. Conventional classifier based on Gaussian function has limitation when dealing with different probability distribution functions and requires large memory for large number of instance. Alternatively, histogram based classifiers are flexible for different probability density function. The aims of PHBayes are to handle large number of instances in datasets with lesser memory requirement, and fast in training and testing phases. The PHBayes depends on portion of the observed histogram that is similar to the probability density function. PHBayes was analyzed using synthetic and real data. Several factors affecting classification accuracy were considered. The PHBayes was compared with other established classifiers and demonstrated higher accurate classification, lesser memory even when dealing with large number of instance, and faster in training and testing phases.

Original languageEnglish
Pages (from-to)126-132
Number of pages7
JournalInternational Journal of Engineering and Technology(UAE)
Volume7
Issue number4
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Learning algorithms
Classifiers
Learning
Data storage equipment
Probability density function
Testing
Probability distributions
Distribution functions
Datasets

Keywords

  • Classification
  • Histogram noise estimation and reduction
  • Histogram probability distribution
  • Naïve Bayes
  • Supervised learning

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science (miscellaneous)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Engineering(all)
  • Hardware and Architecture

Cite this

Partial histogram bayes learning algorithm for classification applications. / Lawend, Haider O.; Muad, Anuar Mikdad; Hussain, Aini.

In: International Journal of Engineering and Technology(UAE), Vol. 7, No. 4, 01.01.2018, p. 126-132.

Research output: Contribution to journalArticle

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