Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Gene selection by sequential search wrapper approaches in microarray cancer class prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Challenges for future intelligent systems in biomedicine
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Artificial Intelligence in Medicine
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In the last years, there has been a large growth in gene expression profiling technologies, which are expected to provide insight into cancer related cellular processes Machine Learning algorithms, which are extensively applied in many areas of the real world, are not still popular in the Bioinformatics community We report on the successful application of the combination of two supervised Machine Learning methods, Bayesian Networks and k Nearest Neighbours algorithms, to cancer class prediction problems in three DNA microarray datasets of huge dimensionality (Colon, Leukemia and NCI-60) The essential gene selection process in microarray domains is performed by a sequential search engine and after used for the Bayesian Network model learning Once the genes are selected for the Bayesian Network paradigm, we combine this paradigm with the well known K NN algorithm in order to improve the classification accuracy.