C4.5: programs for machine learning
C4.5: programs for machine learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Improving classification of microarray data using prototype-based feature selection
ACM SIGKDD Explorations Newsletter
Application of the GA/KNN method to SELDI proteomics data
Bioinformatics
A review of feature selection techniques in bioinformatics
Bioinformatics
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01
Hybrid methods to select informative gene sets in microarray data classification
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Multiagent Framework for Bio-data Mining
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
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Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.