C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Greedy Correlation-Incorporated SVM-Based Algorithm for Gene Selection
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Markov blanket-embedded genetic algorithm for gene selection
Pattern Recognition
Gene selection using genetic algorithm and support vectors machines
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Microarray data usually contains a high level of noisy gene data, the noisy gene data include incorrect, noise and irrelevant genes. Before Microarray data classification takes place, it is desirable to eliminate as much noisy data as possible. An approach to improving the accuracy and efficiency of Microarray data classification is to make a small selection from the large volume of high dimensional gene expression dataset. An effective gene selection helps to clean up the existing Microarray data and therefore the quality of Microarray data has been improved. In this paper, we study the effectiveness of the gene selection technology for Microarray classification methods. We have conducted some experiments on the effectiveness of gene selection for Microarray classification methods such as two benchmark algorithms: SVMs and C4.5. We observed that although in general the performance of SVMs and C4.5 are improved by using the preprocessed datasets rather than the original data sets in terms of accuracy and efficiency, while an inappropriate choice of gene data can only be detrimental to the power of prediction. Our results also implied that with preprocessing, the number of genes selected affects the classification accuracy.