Tzu-Tsung Wong
Orcid: 0000-0001-8132-0214
According to our database1,
Tzu-Tsung Wong
authored at least 22 papers
between 1998 and 2022.
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Bibliography
2022
Linear Approximation of F-Measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets.
IEEE Trans. Knowl. Data Eng., 2022
2021
Multinomial naïve Bayesian classifier with generalized Dirichlet priors for high-dimensional imbalanced data.
Knowl. Based Syst., 2021
2020
IEEE Trans. Knowl. Data Eng., 2020
2017
IEEE Trans. Knowl. Data Eng., 2017
Parametric methods for comparing the performance of two classification algorithms evaluated by k-fold cross validation on multiple data sets.
Pattern Recognit., 2017
2016
An efficient parameter estimation method for generalized Dirichlet priors in naïve Bayesian classifiers with multinomial models.
Pattern Recognit., 2016
2015
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation.
Pattern Recognit., 2015
2014
Generalized Dirichlet priors for Naïve Bayesian classifiers with multinomial models in document classification.
Data Min. Knowl. Discov., 2014
2013
IEEE ACM Trans. Comput. Biol. Bioinform., 2013
2012
Pattern Recognit., 2012
2011
Pattern Recognit., 2011
Expert Syst. Appl., 2011
2010
A Probabilistic mechanism based on clustering analysis and distance measure for subset gene selection.
Expert Syst. Appl., 2010
Parameter estimation for generalized Dirichlet distributions from the sample estimates of the first and the second moments of random variables.
Comput. Stat. Data Anal., 2010
2009
Alternative prior assumptions for improving the performance of naïve Bayesian classifiers.
Data Min. Knowl. Discov., 2009
Improving Naive Bayesian Classifier for Metagenomic Reads Assignment.
Proceedings of the International Conference on Bioinformatics & Computational Biology, 2009
2008
2005
Expert Syst. Appl., 2005
2003
Implications of the Dirichlet Assumption for Discretization of Continuous Variables in Naive Bayesian Classifiers.
Mach. Learn., 2003
2000
Why Discretization Works for Naive Bayesian Classifiers.
Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000
1998