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Averaged Extended Tree Augmented Naive Classifier
2015
Entropy
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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
2016
International Journal of Approximate Reasoning
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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]
2014
Lecture Notes in Computer Science
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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
2021
Mathematics
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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
2013
International Journal of Approximate Reasoning
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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]
2022
arXiv
pre-print
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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?
2015
International Journal of Computational Intelligence Systems
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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
2005
Proceedings of the 22nd international conference on Machine learning - ICML '05
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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
2014
Information Sciences
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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
2005
Machine Learning
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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]
2006
Lecture Notes in Computer Science
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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
2012
International Journal for Information Security Research
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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
2021
Engineering and Scientific International Journal
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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
2008
2008 International Conference on Computational Intelligence for Modelling Control & Automation
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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
2015
Mathematical Problems in Engineering
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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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