Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Sequential Classification for Microarray and Clinical Data
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Learning with many irrelevant features
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Distributed Multi-Feature Recognition Scheme for Greyscale Images
Neural Processing Letters
An agent model for incremental rough set-based rule induction in customer relationship management
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Entropic feature discrimination ability for pattern classification based on neural IAL
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
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Bayesian classifier is an effective and fundamental methodology for solving classification problems. However, it is computationally efficient when all features are considered simultaneously. But sometimes all the features do not contribute significantly to classification. Also the noisy attributes sometimes may decrease the accuracy of classifier. So before classification feature selection is used as a pre-processing step. When the features are added one by one in Bayesian classifier in batch mode in forward selection method huge computation is involved. In this paper, an incremental Bayesian classifier for multivariate normal distribution datasets are proposed. The proposed incremental Bayesian classifier is computationally efficient over batch Bayesian classifier in terms of time. The effectiveness of the proposed incremental Bayesian classifier has been demonstrated through experiments on different datasets. It is found on the basis of experiments that the incremental Bayesian classifier has an equivalent power compared to batch Bayesian classifier in terms classification accuracy. However, the proposed incremental Bayesian classifier has very high speed efficiency in comparison to batch Bayesian classifier.