@article{(Open Science Index):https://publications.waset.org/pdf/16510,
	  title     = {Gene Selection Guided by Feature Interdependence},
	  author    = {Hung-Ming Lai and  Andreas Albrecht and  Kathleen Steinhöfel},
	  country	= {},
	  institution	= {},
	  abstract     = {Cancers could normally be marked by a number of
differentially expressed genes which show enormous potential as
biomarkers for a certain disease. Recent years, cancer classification
based on the investigation of gene expression profiles derived by
high-throughput microarrays has widely been used. The selection of
discriminative genes is, therefore, an essential preprocess step in
carcinogenesis studies. In this paper, we have proposed a novel gene
selector using information-theoretic measures for biological
discovery. This multivariate filter is a four-stage framework through
the analyses of feature relevance, feature interdependence, feature
redundancy-dependence and subset rankings, and having been
examined on the colon cancer data set. Our experimental result show
that the proposed method outperformed other information theorem
based filters in all aspect of classification errors and classification
	    journal   = {International Journal of Biotechnology and Bioengineering},
	  volume    = {7},
	  number    = {7},
	  year      = {2013},
	  pages     = {678 - 684},
	  ee        = {https://publications.waset.org/pdf/16510},
	  url   	= {https://publications.waset.org/vol/79},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 79, 2013},