The nature of statistical learning theory
The nature of statistical learning theory
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Genetic Programming and Evolvable Machines
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
Reinforcement learning estimation of distribution algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Machine learning method for knowledge discovery experimented with otoneurological data
Computer Methods and Programs in Biomedicine
Risk prediction and risk factors identification from imbalanced data with RPMBGA+
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Finding minimal sets of informative genes in microarray data
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Hi-index | 0.00 |
Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples compared to huge number of genes, and the noisy nature of microarray data. After selection of some good genes based on signal-to-noise ratio, unsupervised learning like clustering and supervised learning like k-nearest neighbor (kNN) classifier are widely used in cancer researches to correlate the pathological behavior of cancers with the gene expression levels' differences in cancerous and normal tissues. By applying adaptive searches like Probabilistic Model Building Genetic Algorithm (PMBGA), it may be possible to get a smaller size gene subset that would classify patient samples more accurately than the above methods. In this paper, we propose a new PMBGA based method to extract informative genes from microarray data using Support Vector Machine (SVM) as a classifier. We apply our method to three microarray data sets and present the experimental results. Our method with SVM obtains encouraging results on those data sets as compared with the rank based method using kNN as a classifier.