Machine Learning
Enhancements to the data mining process
Enhancements to the data mining process
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Semi-Naive Bayesian Classifier
EWSL '91 Proceedings of the European Working Session on Machine Learning
Induction of Recursive Bayesian Classifiers
ECML '93 Proceedings of the European Conference on Machine Learning
Adjusted Probability Naive Bayesian Induction
AI '98 Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
An extended tuning method for cost-sensitive regression and forecasting
Decision Support Systems
Bias management of bayesian network classifiers
DS'05 Proceedings of the 8th international conference on Discovery Science
Confidence-Based incremental classification for objects with limited attributes in vertical search
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
| Hi-index | 0.00 |
Naive Bayes is a well known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The iterative Bayes begins with the distribution tables built by the naive Bayes. Those tables are iteratively updated in order to improve the probability class distribution associated with each training example. Experimental evaluation of Iterative Bayes on 27 benchmark datasets shows consistent gains in accuracy. Moreover, the update schema can take costs into account turning the algorithm cost sensitive. Unlike stratification, it is applicable to any number of classes and to arbitrary cost matrices. An interesting side effect of our algorithm is that it shows to be robust to attribute dependencies.