Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/2437

 Title: A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data Authors: Bill, JoFokoue, Ernest Keywords: HDLSSMachine Learning AlgorithmPattern RecognitionClassificationPredictionRegularizationDiscriminant AnalysisSupport Vector MachineKernelsCross ValidationMicroarray Cancer Data Issue Date: 2014 Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences Citation: Serdica Journal of Computing, Vol. 8, No 2, (2014), 137p-168p Abstract: This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied. URI: http://hdl.handle.net/10525/2437 ISSN: 1312-6555 Appears in Collections: Volume 8 Number 2

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