A non parametric Partial Histogram Bayes learning algorithm for classification applications

Lawend, Haider O.; Muad, Anuar Mikdad

doi:10.1109/iccsce.2014.7072685

Cited by 2 publications

(6 citation statements)

References 13 publications

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“…Bayesian based technique like NB, GMMC, and FNB are more suitable. In a previous work [27], our proposed PHBayes demonstrated faster and more accurate classification compared to NB, GMMC, 1 st NN, and NCM. PHBayes need not to keep the number of instance in its memory, but it requires a memory space for the probability density of the observed histogram bins.…”

Section: Introductionmentioning

confidence: 71%

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Partial Histogram Bayes Learning Algorithm for Classification Applications

Lawend

Muad²,

Hussain³

2018

IJET

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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.

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Section: Introductionmentioning

confidence: 71%

“…1 illustrates the concept of using the observed histogram as a means to estimate the probability density. Here, two cases are presented using two different probability density functions [27] as shown in Fig. 1a and 1d.…”

Section: Supervised Partial Histogram Bayesmentioning

confidence: 99%

Partial Histogram Bayes Learning Algorithm for Classification Applications

Lawend

Muad²,

Hussain³

2018

IJET

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“…This allows fPHBayes to work with large and small number of instances and with large and small variances using large histogram resolution. [37]. The reason of using 64 bins of histogram in fPHBayes is to allow it to cover all ranges of histogram lower than 64 bins.…”

Section: Flexible Partial Histogram Bayes Learning (Fphbayes)mentioning

confidence: 99%

“…In this paper, a new Flexible Partial Histogram Bayes learning algorithm (fPHBayes) is proposed which is an improvement of PHBayes. Compared with PHBayes in our previous work [37], the proposed fPHBayes is more accurate to deal with small and large number of instances, more flexible to the class probability distribution and requires fewer parameters to be considered. fPHBayes uses a probability distribution derived from smoothing the observed histogram and performs the classification using Bayesian rule.…”

Section: Introductionmentioning

confidence: 98%

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An Improved Flexible Partial Histogram Bayes Learning Algorithm

Lawend¹,

Muad²,

Hussain³

2018

IJEECS

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<em>This paper presents a proposed supervised classification technique namely flexible partial histogram Bayes (fPHBayes) learning algorithm. In our previous work, partial histogram Bayes (PHBayes) learning algorithm showed some advantages in the aspects of speed and accuracy in classification tasks. However, its accuracy declines when dealing with small number of instances or when the class feature distributes in wide area. In this work, the proposed fPHBayes solves these limitations in order to increase the classification accuracy. fPHBayes was analyzed and compared with PHBayes and other standard learning algorithms like first nearest neighbor, nearest subclass mean, nearest class mean, naive Bayes and Gaussian mixture model classifier. The experiments were performed using both real data and synthetic data considering different number of instances and different variances of Gaussians. The results showed that fPHBayes is more accurate and flexible to deal with different number of instances and different variances of Gaussians as compared to PHBayes.</em>

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A non parametric Partial Histogram Bayes learning algorithm for classification applications

Cited by 2 publications

References 13 publications

Partial Histogram Bayes Learning Algorithm for Classification Applications

Partial Histogram Bayes Learning Algorithm for Classification Applications

An Improved Flexible Partial Histogram Bayes Learning Algorithm

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