# Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees

@inproceedings{Zheng1999LazyBR, title={Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees}, author={Zijian Zheng and Geoffrey I. Webb and K. Ting}, booktitle={ICML}, year={1999} }

Lbr is a lazy semi-naive Bayesian classiier learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classiication. To classify a test example , it creates a conjunctive rule that selects a most appropriate subset of training examples and induces a local naive Bayesian classiier using this subset. Lbr can signii-cantly improve the performance of the naive Bayesian classiier. A bias and variance analysis of Lbr reveals that it signiicantly reduces the… Expand

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This paper explores alternatives to the candidate elimination criterion employed within Lazy Bayesian Rules, demonstrated to provide better overall error reduction than the use of a minimum data subset size criterion. Expand

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A weighted naive Bayes algorithm is proposed, called WANBIA, that selects weights to minimize either the negative conditional log likelihood or the mean squared error objective functions and is found to be a competitive alternative to state of the art classifiers like Random Forest, Logistic Regression and A1DE. Expand

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#### References

SHOWING 1-10 OF 49 REFERENCES

Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid

- Mathematics, Computer Science
- KDD
- 1996

A new algorithm, NBTree, is proposed, which induces a hybrid of decision-tree classifiers and Naive-Bayes classifiers: the decision-Tree nodes contain univariate splits as regular decision-trees, but the leaves contain Naïve-Bayesian classifiers. Expand

Eecient Learning of Selective Bayesian Network Classiiers

- 1996

In this paper, we present a computation-ally eecient method for inducing selective Bayesian network classiiers. Our approach is to use information-theoretic metrics to ef-ciently select a subset of… Expand

Lazy Decision Trees

- Computer Science
- AAAI/IAAI, Vol. 1
- 1996

This work proposes a lazy decision tree algorithm--LAZYDT--that conceptually constructs the "best" decision tree for each test instance, and is robust with respect to missing values without resorting to the complicated methods usually seen in induction of decision trees. Expand

Adjusted Probability Naive Bayesian Induction

- Computer Science
- Australian Joint Conference on Artificial Intelligence
- 1998

The use of this adjusted value in place of the naive Bayesian probability is shown to significantly improve predictive accuracy. Expand

Improving the Performance of Boosting for Naive Bayesian Classification

- Computer Science
- PAKDD
- 1999

The experimental results show that although introducing tree structures into naive Bayesian classification increases the average error of naiveBayesian classification for individual models, boosting naïve Bayesian classifiers with tree structures can achieve significantly lower average error than the naive Bayesesian classifier. Expand

Induction of Selective Bayesian Classifiers

- Computer Science, Mathematics
- UAI
- 1994

This paper embeds the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space of features, hypothesize that this approach will improve asymptotic accuracy in domains that involve correlated features without reducing the rate of learning in ones that do not. Expand

A decision-theoretic generalization of on-line learning and an application to boosting

- Computer Science
- EuroCOLT
- 1995

The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and the multiplicative weightupdate Littlestone Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. Expand

Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier

- Computer Science
- ICML
- 1996

It is shown that the simple Bayesian classi er (SBC) does not in fact assume attribute independence, and can be optimal even when this assumption is violated by a wide margin, and the previously-assumed region of optimality is a second-order in nitesimal fraction of the actual one. Expand

Learning Limited Dependence Bayesian Classifiers

- Mathematics, Computer Science
- KDD
- 1996

A framework for characterizing Bayesian classification methods is presented and a general induction algorithm is presented that allows for traversal of this spectrum depending on the available computational power for carrying out induction and its application in a number of domains with different properties. Expand

Semi-Naive Bayesian Classifier

- Computer Science
- EWSL
- 1991

In the paper the algorithm of the 'naive' Bayesian classifier (that assumes the independence of attributes) is extended to detect the dependencies between attributes. The idea is to optimize the… Expand