Conference Paper
Scaling Up the Accuracy of Bayesian Network Classifiers by MEstimate.
DOI: 10.1007/9783540742050_52 Conference: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 2124, 2007, Proceedings
Source: DBLP
 JSW. 01/2011; 6:13681373.

Article: Learning random forests for ranking.
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ABSTRACT: The random forests (RF) algorithm, which combines the predictions from an ensemble of random trees, has achieved significant improvements in terms of classification accuracy. In many realworld applications, however, ranking is often required in order to make optimal decisions. Thus, we focus our attention on the ranking performance of RF in this paper. Our experimental results based on the entire 36 UC Irvine Machine Learning Repository (UCI) data sets published on the main website of Weka platform show that RF doesnâ€™t perform well in ranking, and is even about the same as a single C4.4 tree. This fact raises the question of whether several improvements to RF can scale up its ranking performance. To answer this question, we single out an improved random forests (IRF) algorithm. Instead of the information gain measure and the maximumlikelihood estimate, the average gain measure and the similarityweighted estimate are used in IRF. Our experiments show that IRF significantly outperforms all the other algorithms used to compare in terms of ranking while maintains the high classification accuracy characterizing RF.Frontiers of Computer Science in China 01/2011; 5:7986. · 0.27 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Frequent Itemsets Mining Classifier (FISC) is an improved Bayesian classifier which averaging all classifiers built by frequent itemsets. Considering that in learning Bayesian network classifier, estimating probabilities from a given set of training examples is crucial, and it has been proved that mestimate can scale up the accuracy of many Bayesian classifiers. Thus, a natural question is whether FISC with mestimate can perform even better. Response to this problem, in this paper, we aim to scale up the accuracy of FISC by mestimate and propose new probability estimation formulas. The experimental results show that the Laplace estimate used in the original FISC performs not very well and our mestimate can greatly scale up the accuracy, it even outperforms other outstanding Bayesian classifiers used to compare. KeywordsFrequent Itemsets Mining Classifierestimating probabilitiesLaplace estimatemestimate10/2010: pages 357364;
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