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Averaged Extended Tree Augmented Naive Classifier

Aaron Meehan, Cassio de Campos
2015 Entropy  
This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive  ...  Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers.  ...  Conflicts of Interest The authors declare no conflict of interest.  ... 
doi:10.3390/e17075085 fatcat:w2tcyxtqwfgp7lkyiznkoz67fa

Learning extended tree augmented naive structures

Cassio P. de Campos, Giorgio Corani, Mauro Scanagatta, Marco Cuccu, Marco Zaffalon
2016 International Journal of Approximate Reasoning  
A range of experiments show that we obtain models with better prediction accuracy than Naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator  ...  This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class  ...  Improving Learning of TANs A simple extension of this algorithm can already learn a forest of treeaugmented naive Bayes structures.  ... 
doi:10.1016/j.ijar.2015.04.006 fatcat:pylcdsw22rghhg3sy5wfko5gym

Extended Tree Augmented Naive Classifier [chapter]

Cassio P. de Campos, Marco Cuccu, Giorgio Corani, Marco Zaffalon
2014 Lecture Notes in Computer Science  
This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class  ...  A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.  ...  Improving Learning of TANs A simple extension of this algorithm can already learn a forest of tree-augmented naive Bayes structures.  ... 
doi:10.1007/978-3-319-11433-0_12 fatcat:lg7ykxtxyvgc5bj62uwc5w7xvi

Attribute Selecting in Tree-Augmented Naive Bayes by Cross Validation Risk Minimization

Shenglei Chen, Zhonghui Zhang, Linyuan Liu
2021 Mathematics  
In this paper, we propose an attribute Selective Tree-Augmented Naive Bayes (STAN) algorithm which builds a sequence of approximate models each involving only the top certain attributes and searches the  ...  As an important improvement to naive Bayes, Tree-Augmented Naive Bayes (TAN) exhibits excellent classification performance and efficiency since it allows that every attribute depends on at most one other  ...  [11] proposed an extended version of the well-known tree-augmented naive Bayes.  ... 
doi:10.3390/math9202564 fatcat:jstlgz62gndhjeyj37szoncedq

LTC: A latent tree approach to classification

Yi Wang, Nevin L. Zhang, Tao Chen, Leonard K.M. Poon
2013 International Journal of Approximate Reasoning  
An LTC represents each class-conditional distribution of attributes using a latent tree model, and uses Bayes rule to make prediction.  ...  In this paper, we study the usefulness of latent tree models in another paradigm, namely supervised learning. We propose a novel generative classifier called latent tree classifier (LTC).  ...  We are also grateful to the anonymous reviewers for their valuable suggestions on an earlier version of this paper.  ... 
doi:10.1016/j.ijar.2012.06.024 fatcat:vcfx2dzfwjcvjhf3im6tx6ab74

Hierarchical Dependency Constrained Tree Augmented Naive Bayes Classifiers for Hierarchical Feature Spaces [article]

Cen Wan, Alex A. Freitas
2022 arXiv   pre-print
The Tree Augmented Naive Bayes (TAN) classifier is a type of probabilistic graphical model that constructs a single-parent dependency tree to estimate the distribution of the data.  ...  In this work, we propose two novel Hierarchical dependency-based Tree Augmented Naive Bayes algorithms, i.e. Hie-TAN and Hie-TAN-Lite.  ...  Background Conventional Tree Augmented Naïve Bayes Tree Augmented Naïve Bayes (TAN) is a type of seminaïve Bayes classification algorithm.  ... 
arXiv:2202.04105v1 fatcat:tf6ipjkkkjdqlkwrxxnbnfpuqm

Locally Weighted Learning: How and When Does it Work in Bayesian Networks?

Jia Wu, Bi Wu, Shirui Pan, Haishuai Wang, Zhihua Cai
2015 International Journal of Computational Intelligence Systems  
method for complex BNCs, e.g., tree-augmented naive Bayes (TAN), averaged one-dependence estimators AODE and hidden naive Bayes (HNB), measured by classification accuracy (ACC) and the area under the  ...  And this type of approach has been proved to achieve good performance for naive Bayes, a BNC with simple structure.  ...  Tree Augmented Naive Bayes (TAN) 4 appears as a natural extension to the naive Bayes classifier. And a Naive Bayes/Decision-Tree Hybrid (NBTree) 15 has combined a decision tree with naive Bayes.  ... 
doi:10.1080/18756891.2015.1129579 fatcat:hxrmttxq2bdsjivhipiiz6wssa

Augmenting naive Bayes for ranking

Harry Zhang, Liangxiao Jiang, Jiang Su
2005 Proceedings of the 22nd international conference on Machine learning - ICML '05  
Then, we propose a new approach to augmenting naive Bayes for generating accurate ranking, called hidden naive Bayes (HNB).  ...  Our experiments show that HNB outperforms naive Bayes, SBC, boosted naive Bayes, NBTree, and TAN significantly, and performs slightly better than AODE in ranking.  ...  The most recent work on improving naive Bayes is AODE (averaged one-dependence estimators) (Webb et al., 2005) .  ... 
doi:10.1145/1102351.1102480 dblp:conf/icml/ZhangJS05 fatcat:g3tewtl6jvehxaniq3iedtpukq

Domains of competence of the semi-naive Bayesian network classifiers

M. Julia Flores, José A. Gámez, Ana M. Martínez
2014 Information Sciences  
This study is carried out on continuous and discrete domains for naive Bayes and Averaged One-Dependence Estimators (AODE), which are two widely used incremental classifiers that provide some of the best  ...  Hence, the proposal of new classifiers can be seen as an attempt to cover new areas of the complexity space of datasets, or even to compete with those previously assigned to others.  ...  is being extensively used for machine learning and data mining in a variety of scientific applications, especially the Averaged One-Dependence Estimator 2 (AODE) [69] .  ... 
doi:10.1016/j.ins.2013.10.007 fatcat:tjw6o2if4jczjo3sorgaijbkqy

Not So Naive Bayes: Aggregating One-Dependence Estimators

Geoffrey I. Webb, Janice R. Boughton, Zhihai Wang
2005 Machine Learning  
We present a new approach to weakening the attribute independence assumption by averaging all of a constrained class of classifiers.  ...  Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, both LBR and super-parent TAN have demonstrated remarkable error performance.  ...  Acknowledgements We are very grateful to Fei Zheng and Shane Butler for valuable comments on drafts of this paper.  ... 
doi:10.1007/s10994-005-4258-6 fatcat:mkbtmq4abne5rdblfsn42wlpzm

Voting Massive Collections of Bayesian Network Classifiers for Data Streams [chapter]

Remco R. Bouckaert
2006 Lecture Notes in Computer Science  
We present a new method for voting exponential (in the number of attributes) size sets of Bayesian classifiers in polynomial time with polynomial memory requirements.  ...  Training is linear in the number of instances in the dataset and can be performed incrementally. This allows the collection to learn from massive data streams.  ...  Thus, the graph among the attributes forms a tree, hence the name tree augmented naive Bayes (TAN) [3] .  ... 
doi:10.1007/11941439_28 fatcat:imjfuqxxijdjrpdwf4c2dpzynm

One-Dependence Estimators for Accurate Detection of Anomalous Network Traffic

Zubair A. Baig, Abdulrhman S. Shaheen, Radwan AbdelAal
2012 International Journal for Information Security Research  
Average One Dependence Estimator (AODE).  ...  Naïve Bayes simulation.  ...  Acknowledgements The authors wish to acknowledge the continuing support and facilities provided by King Fahd University of Petroleum and Minerals to conduct research. References  ... 
doi:10.20533/ijisr.2042.4639.2012.0025 fatcat:gmapirxp6jhopppw6vx35zb3ia

Cervical Cancer Cell Prediction using Machine Learning Classification Algorithms

Prianka R R, Celine Kavida A, Bibin M R
2021 Engineering and Scientific International Journal  
Classification and Regression Tree (CART) is a simple decision tree algorithm that is used to create a decision tree of a given set.  ...  In this paper, to make the detection process a portion faster and accurate machine learning techniques such as Decision Stump, C4.5 and Averaged One Dependence Estimators (AODE) for novel NCBI cervical  ...  Pruning the tree after being created. Pessimistic prediction error. Sub-tree raising. Naive AODE Algorithm The performance of ODE is well with a large number of training or input data items.  ... 
doi:10.30726/esij/v8.i1.2021.81006 fatcat:fly2yo66qfdlfaqhkkwwwve3xu

Exploring the Design Space of Symbolic Music Genre Classification Using Data Mining Techniques

Christian Kofod, Daniel Ortiz-Arroyo
2008 2008 International Conference on Computational Intelligence for Modelling Control & Automation  
Our experimental results indicate that our system constructed with the best performing combination of data mining preprocessing components together with a Naive Bayes-based classifier is capable of outperforming  ...  Additionally, we employ a variety of probabilistic classifiers and ensembles. We compare the results produced by our best classifier with those obtained by more complex state of the art classifiers.  ...  Acknowledgments The authors would like to thank especially Cory McKay, from McGill University, Canada, for supplying two of his MIDI repositories and the jSymbolic feature extractor.  ... 
doi:10.1109/cimca.2008.223 dblp:conf/cimca/KofodA08 fatcat:i6ghcstz7zdzzoi7bqyvj6x6de

Bayesian Prediction Model Based on Attribute Weighting and Kernel Density Estimations

Zhong-Liang Xiang, Xiang-Ru Yu, Dae-Ki Kang
2015 Mathematical Problems in Engineering  
Experiments have been conducted on UCI benchmark datasets and the accuracy of our proposed learner has been compared with that of standard naïve Bayes.  ...  Although naïve Bayes learner has been proven to show reasonable performance in machine learning, it often suffers from a few problems with handling real world data.  ...  After the training stage, AODE outputs an average one-dependence estimator. AODE is a lazy method of structure extension of Bayesian network. Jiang et al.  ... 
doi:10.1155/2015/170324 fatcat:soqqu726xfaojm7qpx6tdi3qdy
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