Elements of information theory
Elements of information theory
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Data mining: concepts and techniques
Data mining: concepts and techniques
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Recognizing End-User Transactions in Performance Management
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A methodology for analyzing SAGE libraries for cancer profiling
ACM Transactions on Information Systems (TOIS)
Multinomial event naive Bayesian modeling for SAGE data classification
Computational Statistics
Event models for tumor classification with SAGE gene expression data
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Flexible case-based retrieval for comparative genomics
Applied Intelligence
Hi-index | 0.00 |
Cancer class prediction and discovery is beneficial to imperfect non-automated cancer diagnoses which affect patient cancer treatments. Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling an automatic, precise and early diagnosis. A promising application of SAGE gene expression data is classification of cancers. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE gene expression profiles. The event models based methods are compared with the standard Naïve Bayes method. Both binary classification and multicategory classification are investigated. Experiments results on several SAGE datasets show that event models are better than standard Naïve Bayes in general. Normalized Information Gain (NIG), an extension of Information Gain (IG), is proposed for gene selection. The impact of gene correlation on the classification performance is investigated.