Building Naïve Bayes Classifiers with High-Dimensional and Small-Sized Data Sets

[1]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[2]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[3]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[4]  Joaquín Dopazo,et al.  Papers on normalization, variable selection, classification or clustering of microarray data , 2009, Bioinform..

[5]  Ludmila I. Kuncheva,et al.  On the optimality of Naïve Bayes with dependent binary features , 2006, Pattern Recognit. Lett..

[6]  Remco R. Bouckaert.  Naive Bayes Classifiers That Perform Well with Continuous Variables , 2004, Australian Conference on Artificial Intelligence.

[7]  Shyam Visweswaran,et al.  Improving Classification Performance with Discretization on Biomedical Datasets , 2008, AMIA.

[8]  R. Gentleman,et al.  Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. , 2004, Blood.

[9]  B. Chandra,et al.  Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data , 2011, Expert Syst. Appl..

[10]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[11]  Jae Won Lee,et al.  An extensive comparison of recent classification tools applied to microarray data , 2004, Comput. Stat. Data Anal..

[12]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[13]  Li Wang,et al.  Hybrid huberized support vector machines for microarray classification and gene selection , 2008, Bioinform..

[14]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.