Not So Naive Bayes: Aggregating One-Dependence Estimators

Webb, Geoffrey I.; Boughton, Janice R.; Wang, Zhihai

doi:10.1007/s10994-005-4258-6

Cited by 589 publications

(371 citation statements)

References 21 publications

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“…We confirmed results from [3] and [8] that classification performance of TAN and AODE is better than naive Bayes and that AODE tends to perform similar to TAN but with lower train time. Further, of the collections (FANC, SPC, SPCr and TC) classified all consistently better than naive Bayes, except for the labor dataset, which is due to the small number of instances (just 57 in total).…”

Section: Small Data Setssupporting

confidence: 89%

“…AODE classifiers [8] uses the super parent idea to build a collection of n Bayesian classifiers by letting each of the attributes be super parent in one of the networks. The class is predicted by taking the sum of the probabilistic votes of each of the networks.…”

Section: Bayesian Network Classifiersmentioning

confidence: 99%

“…Averaged One-Dependence Estimators (AODE) [8] can be considered as voting of various Bayesian classifiers, one for each attribute in the data set. It performs well on both small and large data sets and can be trained incrementally.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Voting Massive Collections of Bayesian Network Classifiers for Data Streams

Bouckaert

2006

Lecture Notes in Computer Science

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Abstract. 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. The method allows for flexibility in balancing computational complexity, memory requirements and classification performance. Unlike many other incremental Bayesian methods, all statistics kept in memory are directly used in classification. Experimental results show that the classifiers perform well on both small and very large data sets, and that classification performance can be weighed against computational and memory costs.

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Section: Small Data Setssupporting

confidence: 89%

Section: Bayesian Network Classifiersmentioning

confidence: 99%

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Voting Massive Collections of Bayesian Network Classifiers for Data Streams

Bouckaert

2006

Lecture Notes in Computer Science

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“…AODE, is a technique that weaken NB's attribute independence assumption [4]. The classifier selects the class by formula (3).…”

Section: Aodementioning

confidence: 99%

The Research of predicting the Cervical Cancer Basing on Bayesian Algorithm

Hui¹,

Jiang²,

Zhou³

et al. 2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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Cervical cancer remains a significant cause of mortality in low-income country. It is necessary to find a most effective machine learning algorithm to predict the cervical cancer. Our research attempts to help doctor to judge whether a patient develops cervical cancer. Thus, we use Bayesian Classification, decision tree and Support Vector Machine to analysis the dataset. As a result, we compared 0-1 loss classification error rate of these algorithm, whose error rate is the least so it is the best. And the conclusion is that AODE is the most suitable algorithm of these.

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“…• An artificial neural network (ANN), usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks [8]. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation.…”

Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

Hormozi¹,

Hormozi²,

Nohooji³

2012

IJMLC

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Abstract-Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques used in robotic manipulators. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.Index Terms-Machine learning, adaptive control, repetitive control, robot manipulators. I. INTRODUCTIONMachine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data [1]. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Machine learning, like all subjects in artificial intelligence, require cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive science, data mining, adaptive control, computational neuroscience and theoretical computer science [2]. In this paper we are focused on learning algorithms for robot manipulators. II. MACHINE LEARNING ALGORITHMSMachine learning algorithms are organized into a taxonomy based on the desired outcome of the algorithm.Supervised learning generates a function that maps inputs to desired outputs. For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function for robot manipulators [1].Unsupervised learning models a set of inputs, like clustering [1].Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier in manipulators [3].Reinforcement learning learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm [1] and [3].Transduction tries to predict new outputs based on training inputs, training outputs, an...

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Not So Naive Bayes: Aggregating One-Dependence Estimators

Cited by 589 publications

References 21 publications

Voting Massive Collections of Bayesian Network Classifiers for Data Streams

Voting Massive Collections of Bayesian Network Classifiers for Data Streams

The Research of predicting the Cervical Cancer Basing on Bayesian Algorithm

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

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