Database-friendly random projections
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Attribute Selection Methods Comparison for Classification of Diffuse Large B-Cell Lymphoma
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Feature Selection for Gene Expression Using Model-Based Entropy
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dimensionality Reduction in Gene Expression Database through the Random Projection Method
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
| Hi-index | 12.05 |
Dimensionality reduction has been applied in the most different areas, among which the data analysis of gene expression obtained with the microarray approach. The data involved in this problem is challenging for machine learning algorithms due to a small number of samples and a high number of attributes. This paper proposes a preprocessing phase by means of attribute selection and random projection method in microarray data. Experimental results are promising and show that the use of these methods improves the performance of classification algorithms.