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Improving Classification In Bayesian Networks Using Structural Learning

Hong Choon Ong
2011 Zenodo  
However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem.  ...  In this study, the use of structural learning in Bayesian Network is proposed to be applied where there are relationships between the features when using the Naïve Bayes.  ...  The structure of the Naïve Bayes network is shown in Figure 1 .  ... 
doi:10.5281/zenodo.1335261 fatcat:4i547utuirg6ljckulexixnqsa

New approach using Bayesian Network to improve content based image classification systems [article]

Khlifia jayech, mohamed ali mahjoub
2012 arXiv   pre-print
This paper proposes a new approach based on augmented naive Bayes for image classification. Initially, each image is cutting in a whole of blocks. For each block, we compute a vector of descriptors.  ...  Finally, we propose three variants of Bayesian Networks such as Naive Bayesian Network (NB), Tree Augmented Naive Bayes (TAN) and Forest Augmented Naive Bayes (FAN) to classify the image using the vector  ...  In particular, the aim is to improve Naïve Bayes by removing some of the unwarranted independence relations among features and hence we extend Naïve Bayes structure shown in figure 7 by implementing the  ... 
arXiv:1204.1631v1 fatcat:pz2g5ozjanfjfpmye7zs7ybyeu

Effective Discretization and Hybrid feature selection using Naïve Bayesian classifier for Medical datamining

Ranjit Abraham, Jay B. Simha, S. Sitharama Iyengar
2009 International Journal of Computational Intelligence Research  
Bayes as well as some popular non-Naïve Bayesian statistical classifiers.  ...  Our experimental results suggest that on an average, with Minimum Description Length (MDL) discretization the Naïve Bayes Classifier seems to be the best performer compared to popular variants of Naïve  ...  Figure 1 . 1 Structure of Naïve Bayes Figure 2 . 2 Structural representation of Tree Augmented Naïve Bayes (TAN) Boosting involves learning a series of classifiers, where each classifier in the series  ... 
doi:10.5019/j.ijcir.2009.175 fatcat:k6r7ibls2jce3mqmji5a2uuuli

Quranic Reciter Recognition: A Machine Learning Approach

Rehan Ullah Khan, Ali Mustafa Qamar, Mohammed Hadwan
2019 Advances in Science, Technology and Engineering Systems  
In both cases, the features are learned with the classical machine learning which includes the Naïve Bayes, J48, and the Random Forest.  ...  In such a case, we get 88% recognition accuracy with the Naïve Bayes and the Random Forest showing that Qari can be effectively recognized from the recitation of the Quranic verses.  ...  Acknowledgment The work in this article is funded in its entirety by the Deanship of Scientific Research (SRD), Project number: 3600-coc-2018-1-14-S at the Qassim University, Kingdom of Saudi Arabia.  ... 
doi:10.25046/aj040621 fatcat:2oai74h7kfbrtksqtmauvnnz64

Clustering and Bayesian network for image of faces classification

Khlifia Jayech, Mohamed Ali
2011 International Journal of Advanced Computer Science and Applications  
In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR).  ...  The results demonstrate FA outperforms than GFA , B, GTA and TA in the overall classification accuracy.  ...  In particular, the aim is to improve Naïve Bayes by removing some of the unwarranted independence ong features and hence we extend Naïve Bayes structure by implementing the Tree Augmented Naïve Bayes.  ... 
doi:10.14569/specialissue.2011.010105 fatcat:5gfzha4hsjbu3cienesvbbgguq

Struct-NB: predicting protein-RNA binding sites using structural features

Fadi Towfic, Cornelia Caragea, David C. Gemperline, Drena Dobbs, Vasant Honavar
2010 International Journal of Data Mining and Bioinformatics  
We compare the performance of Naïve Bayes and Gaussian Naïve Bayes with that of Struct-NB classifiers on the 147 protein-RNA dataset using sequence and structural features respectively as input to the  ...  The results of our experiments show that Struct-NB outperforms Naïve Bayes and Gaussian Naïve Bayes on the problem of predicting the protein-RNA binding interfaces in a protein sequence in terms of a range  ...  In addition, we compared the ROC curves of Naïve Bayes using sequence features with that of Gaussian Naïve Bayes using structural features.  ... 
doi:10.1504/ijdmb.2010.030965 pmid:20300450 pmcid:PMC2840657 fatcat:6kcslp2m4bfc7gnfx7ocprezs4

Clustering and Bayesian network for image of faces classification [article]

Khlifia Jayech, Mohamed Ali Mahjoub
2012 arXiv   pre-print
In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR).  ...  The objective is to reduce the error in the classification phase. Second, we cut the image in a whole of blocks. For each block, we compute a vector of descriptors.  ...  In particular, the aim is to improve Naïve Bayes by removing some of the unwarranted independence relations among features and hence we extend Naïve Bayes structure by implementing the Tree Augmented Naïve  ... 
arXiv:1204.1679v1 fatcat:p5xahddzknagbakg4clbwrkjji

Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS

Rosa Blanco, Iñaki Inza, Marisa Merino, Jorge Quiroga, Pedro Larrañaga
2005 Journal of Biomedical Informatics  
In this paper, filter and wrapper approaches based on the feature subset selection are adapted to induce Bayesian classifiers (naive Bayes, selective naive Bayes, semi naive Bayes, tree augmented naive  ...  Feature subset selection is useful due to the heterogeneity of the medical databases where not all the variables are required to perform the classification.  ...  Fig. 6 shows the structure of a semi naive Bayes classification model.  ... 
doi:10.1016/j.jbi.2005.05.004 pmid:15967731 fatcat:ubf6ljmodfchjh5vqjl5owtjie

Improved Naive Bayes Classification for Joint Investment Plan

Mufda Jameel Alrawashdeh
2022 WSEAS Transactions on Mathematics  
Hence, this paper uses a method to improve the accuracy of Naïve Bayes approach by using a learning structure of feature variables in the model and apply it to joint investment plan applications.  ...  Naïve Bayes is an ideal approach to aid the approval or rejection of this collaboration by the decision maker. The approach assumes independencies among the variables.  ...  We improved the Naïve Bayes by learning the structure and the relationship among the feature variables.  ... 
doi:10.37394/23206.2022.21.6 fatcat:qc5ejosu4jenpni2o5fy2y3mnm

An Efficient Method for Internet Traffic Classification and Identification using Statistical Features

Remya Raveendran, Raghi Menon
2015 International Journal of Engineering Research and  
For the classification process Naïve Bayes with Discretization is used. The proposed scheme is compared with three other Bayesian models.  ...  Traffic Classification is a method of categorizing the computer network traffic based on various features observed passively in the traffic into a number of traffic classes.  ...  No other connections are allowed in a Naïve-Bayes structure. Naïve-Bayes has been used as an effective classifier. .  ... 
doi:10.17577/ijertv4is070268 fatcat:ax5hoo6plvc25ht7otws2g5jni

Classification of Radar Returns from Ionosphere Using NB-Tree and CFS

Aung Nway Oo
2018 International Journal of Trend in Scientific Research and Development  
This paper present an experimental different classifiers namely Naïve Bayes (NB) and NB-Tree for classification of radar returns from Ionosphere dataset.  ...  Correlation-based Feature Subset Selection (CFS) is also used for attribute selection. The purpose is to achieve the efficie classification.  ...  We outline overview of Naïve Bayes in section 3 and NB-Tree classifier in Section 4. Section 5 presents the based Feature Subset Selection (CFS).  ... 
doi:10.31142/ijtsrd17126 fatcat:sr23kfgqazgcrclcnevdotaz5q

An Improvement to Naive Bayes for Text Classification

Wei Zhang, Feng Gao
2011 Procedia Engineering  
Naïve Bayes classifiers which are widely used for text classification in machine learning are based on the conditional probability of features belonging to a class, which the features are selected by feature  ...  Illustrative examples show that the proposed meth-od indeed improves the performance of naïve Bayes classifier.  ...  Acknowledgements The research is supported in part by the National Natural Science Foundation (60633020, 60802056, 60921003,60905018), National Science Fund for Distinguished Young Scholars (60825202),  ... 
doi:10.1016/j.proeng.2011.08.404 fatcat:xsd5m364djeztnsv7vujxiixjm

Detection and Classification of Brain Tumor Using Naïve Bayes and J48

Nora Naik et al., Nora Naik et al.,
2019 International Journal of Computer Science Engineering and Information Technology Research  
To avoid this problem, the proposed system focuses on detecting and classifying whether brain tumor is malignant or benign based on the features extracted from the tumor region with lesser time and higher  ...  accuracy in comparison to the manual analysis.  ...  In order to classify the image into benign or malignant we make use of two classification algorithms: Naïve Bayes and J48.  ... 
doi:10.24247/ijcseitrdec20194 fatcat:4bpq36zk4vbklnstwisqaddica

Malicious URL Detection and Identification

Anjali B.Sayamber, Arati M. Dixit
2014 International Journal of Computer Applications  
The proposed model based on Naive Bayes is supported by clustering and classification technique.  ...  An alternative approach has been proposed which uses a Naïve Bayes classifier for an automated classification and detection of malicious URLs.  ...  of features, and finally SVM and Naïve Bayes are classifier used for the URL classification.  ... 
doi:10.5120/17464-8247 fatcat:vis4kcdirvdhlelhiy4yinrbjy

Medical Datamining with a New Algorithm for Feature Selection and Naive Bayesian Classifier

Ranjit Abraham, Jay B. Simha, S. S. Iyengar
2007 10th International Conference on Information Technology (ICIT 2007)  
In this paper we propose a new feature selection algorithm to improve the classification accuracy of Naïve Bayes with respect to medical datasets.  ...  Much research work in datamining has gone into improving the predictive accuracy of statistical classifiers by applying the techniques of discretization and feature selection.  ...  Hence learning restricted structures such as naïve Bayes is more practical. The naïve Bayesian classifier represented as a BN has the simplest structure.  ... 
doi:10.1109/icit.2007.41 dblp:conf/cit/AbrahamSI07 fatcat:qjwicggccfeijgalsn4yjuul6a
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