CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 vol.9 Issue No.04 - July-Aug.
Issue No.04 - July-Aug. (2012 vol.9)
Hau-San Wong , Dept. of Comput. Sci., City Univ. of Hong Kong, Kowloon, China
Shaohong Zhang , Dept. of Comput. Sci., Guangzhou Univ., Guangzhou, China
Ying Shen , Dept. of Comput. Sci., City Univ. of Hong Kong, Kowloon, China
Dongqing Xie , Dept. of Comput. Sci., Guangzhou Univ., Guangzhou, China
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.34
Feature selection is widely established as one of the fundamental computational techniques in mining microarray data. Due to the lack of categorized information in practice, unsupervised feature selection is more practically important but correspondingly more difficult. Motivated by the cluster ensemble techniques, which combine multiple clustering solutions into a consensus solution of higher accuracy and stability, recent efforts in unsupervised feature selection proposed to use these consensus solutions as oracles. However, these methods are dependent on both the particular cluster ensemble algorithm used and the knowledge of the true cluster number. These methods will be unsuitable when the true cluster number is not available, which is common in practice. In view of the above problems, a new unsupervised feature ranking method is proposed to evaluate the importance of the features based on consensus affinity. Different from previous works, our method compares the corresponding affinity of each feature between a pair of instances based on the consensus matrix of clustering solutions. As a result, our method alleviates the need to know the true number of clusters and the dependence on particular cluster ensemble approaches as in previous works. Experiments on real gene expression data sets demonstrate significant improvement of the feature ranking results when compared to several state-of-the-art techniques.
lab-on-a-chip, biology computing, data mining, feature extraction, genetic algorithms, genetics, multiple clustering solutions, unsupervised feature ranking method, gene expression data, consensus affinity, feature selection, fundamental computational techniques, microarray data mining, cluster ensemble techniques, Clustering algorithms, Gene expression, Indexes, Bioinformatics, Partitioning algorithms, Principal component analysis, Laplace equations, cluster ensembles., Unsupervised feature ranking, gene selection
Hau-San Wong, Shaohong Zhang, Ying Shen, Dongqing Xie, "A New Unsupervised Feature Ranking Method for Gene Expression Data Based on Consensus Affinity", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 4, pp. 1257-1263, July-Aug. 2012, doi:10.1109/TCBB.2012.34