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Nonnegative Least-Squares Methods for the Classification of High-Dimensional Biological Data
March-April 2013 (vol. 10 no. 2)
pp. 447-456
Yifeng Li, Sch. of Comput. Sci., Univ. of Windsor, Windsor, ON, Canada
Alioune Ngom, Sch. of Comput. Sci., Univ. of Windsor, Windsor, ON, Canada
Microarray data can be used to detect diseases and predict responses to therapies through classification models. However, the high dimensionality and low sample size of such data result in many computational problems such as reduced prediction accuracy and slow classification speed. In this paper, we propose a novel family of nonnegative least-squares classifiers for high-dimensional microarray gene expression and comparative genomic hybridization data. Our approaches are based on combining the advantages of using local learning, transductive learning, and ensemble learning, for better prediction performance. To study the performances of our methods, we performed computational experiments on 17 well-known data sets with diverse characteristics. We have also performed statistical comparisons with many classification techniques including the well-performing SVM approach and two related but recent methods proposed in literature. Experimental results show that our approaches are faster and achieve generally a better prediction performance over compared methods.
Index Terms:
support vector machines,bioinformatics,genetics,genomics,lab-on-a-chip,learning (artificial intelligence),least mean squares methods,statistical analysis,support vector machine,nonnegative least-squares method,high-dimensional biological data classification,microarray data,disease detection,therapy response prediction,classification model,computational problem,reduced prediction accuracy,nonnegative least-squares classifier,high-dimensional microarray gene expression,comparative genomic hybridization data,local learning,transductive learning,ensemble learning,computational experiment,statistical comparison,SVM approach,Algorithms,Classificaiton,Least squares methods,Diseases,Medical information systems,classifier design and evaluation,algorithms,Medicine
Citation:
Yifeng Li, Alioune Ngom, "Nonnegative Least-Squares Methods for the Classification of High-Dimensional Biological Data," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 447-456, March-April 2013, doi:10.1109/TCBB.2013.30
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