Nonnegative least-squares methods for the classification of high-dimensional biological data.
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.
School of Computer Science, University of Windsor, 5115 Lambton Tower, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada.
Yifeng
Y
Li
Alioune
A
Ngom
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D016018:Least-Squares Analysis; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine