A hybrid discretization method for naïve Bayesian classifiers

Wong, Tzu-Tsung

doi:10.1016/j.patcog.2011.12.014

Cited by 40 publications

(27 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The simplicity of Naïve Bayes classifiers also ensures computational efficiency (Almeida, Almeida, & Yamakami, 2011). Although the assumption of independence among features is more often than not violated in practical datasets, Naïve Bayesian generally gives comparable performance against much more sophisticated classifiers (Jin, Lu, & Ling, 2003;Rish, 2001;Wong, 2012).…”

Section: Naïve Bayesmentioning

confidence: 99%

Sample re-weighting hyper box classifier for multi-class data classification

Yang

Liu

Tsoka

et al. 2015

Computers & Industrial Engineering

View full text Add to dashboard Cite

a b s t r a c tIn this work, we propose two novel classifiers for multi-class classification problems using mathematical programming optimisation techniques. A hyper box-based classifier (Xu & Papageorgiou, 2009) that iteratively constructs hyper boxes to enclose samples of different classes has been adopted. We firstly propose a new solution procedure that updates the sample weights during each iteration, which tweaks the model to favour those difficult samples in the next iteration and therefore achieves a better final solution. Through a number of real world data classification problems, we demonstrate that the proposed refined classifier results in consistently good classification performance, outperforming the original hyper box classifier and a number of other state-of-the-art classifiers.Furthermore, we introduce a simple data space partition method to reduce the computational cost of the proposed sample re-weighting hyper box classifier. The partition method partitions the original dataset into two disjoint regions, followed by training sample re-weighting hyper box classifier for each region respectively. Through some real world datasets, we demonstrate the data space partition method considerably reduces the computational cost while maintaining the level of prediction accuracies.

show abstract

Section: Naïve Bayesmentioning

confidence: 99%

Sample re-weighting hyper box classifier for multi-class data classification

Yang

Liu

Tsoka

et al. 2015

Computers & Industrial Engineering

View full text Add to dashboard Cite

show abstract

“…They used the propositionalized dataset and PAT-Table generated by the PAT-learner to build naïve Bayes classifiers. [16] focused on the discretization of attributes to improve naïve Bayes classification. Wong proposed a hybrid method for continuous attributes, and mentioned that the discretization of continuous attributes in a dataset using different methods can improve the performance of naïve Bayes learning.…”

Section: Related Workmentioning

confidence: 99%

“…Wong proposed a hybrid method for continuous attributes, and mentioned that the discretization of continuous attributes in a dataset using different methods can improve the performance of naïve Bayes learning. Additionally, [16] provided a nonparametric measure to evaluate the level of dependence between a continuous attribute and the class.…”

Section: Related Workmentioning

confidence: 99%

Experimental analysis of naïve Bayes classifier based on an attribute weighting framework with smooth kernel density estimations

Xiang

Kang

2015

Appl Intell

View full text Add to dashboard Cite

Naïve Bayes learners are widely used, efficient, and effective supervised learning methods for labeled datasets in noisy environments. It has been shown that naïve Bayes learners produce reasonable performance compared with other machine learning algorithms. However, the conditional independence assumption of naïve Bayes learning imposes restrictions on the handling of real-world data. To relax the independence assumption, we propose a smooth kernel to augment weights for the likelihood estimation. We then select an attribute weighting method that uses the mutual information metric to cooperate with the proposed framework. A series of experiments are conducted on 17 UCI benchmark datasets to compare the accuracy of the proposed learner against that of other methods that employ a relaxed conditional independence assumption. The results demonstrate the effectiveness and efficiency of our proposed learning algorithm. The overall results also indicate the superiority of attribute-weighting methods over those that attempt to determine the structure of the network.

show abstract

“…In [9], [10] SNB (Selective Naïve Bayes) is used for filtering and ranking the attribute of medical dataset which gives the better performance. The prior of single attribute is measured by adding the new attribute with the existing set and comparing the accuracy of the current and previous sets.…”

Section: Existing Work 71 Feature Selection Algorithms On Medical Dmentioning

confidence: 99%

Analysis of Feature Selection Algorithms on Classification: A Survey

Vanaja¹,

Kumar²

2014

IJCA

View full text Add to dashboard Cite

The aim of this paper is to discuss about various feature selection algorithms applied on different datasets to select the relevant features to classify data into binary and multi class in order to improve the accuracy of the classifier. Recent researches in medical diagnose uses the different kind of classification algorithms to diagnose the disease. For predicting the disease, the classification algorithm produces the result as binary class. When there is a multiclass dataset, the classification algorithm reduces the dataset into a binary class for simplification purpose by using any one of the data reduction methods and the algorithm is applied for prediction. When data reduction on original dataset is carried out, the quality of the data may degrade and the accuracy of an algorithm will get affected. To maintain the effectiveness of the data, the multiclass data must be treated with its original form without maximum reduction, and the algorithm can be applied on the dataset for producing maximum accuracy. Dataset with maximum number of attributes like thousands must incorporate the best feature selection algorithm for selecting the relevant features to reduce the space and time complexity. The performance of Classification algorithm is estimated by how accurately it predicts the individual class on particular dataset. The accuracy constrain mainly depends on the selection of appropriate features from the original dataset. The feature selection algorithms play an important role in classification for better performance. The feature selection is one of the preprocessing techniques in the classification. This research paper deals with different feature selection algorithms and their performance on different dataset. General TermsFeature selection algorithm, Medical dataset, Accuracy, Filter method, Wrapper method, embedded method.

show abstract

A hybrid discretization method for naïve Bayesian classifiers

Cited by 40 publications

References 11 publications

Sample re-weighting hyper box classifier for multi-class data classification

Sample re-weighting hyper box classifier for multi-class data classification

Experimental analysis of naïve Bayes classifier based on an attribute weighting framework with smooth kernel density estimations

Analysis of Feature Selection Algorithms on Classification: A Survey

Contact Info

Product

Resources

About