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A Bayesian beta kernel model for binary classification and online learning problems
Author(s) -
MacKenzie Cameron A.,
Trafalis Theodore B.,
Barker Kash
Publication year - 2014
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11241
Subject(s) - artificial intelligence , computer science , machine learning , bayesian linear regression , kernel (algebra) , support vector machine , kernel method , radial basis function kernel , beta distribution , polynomial kernel , bayesian probability , pattern recognition (psychology) , bayesian inference , mathematics , data mining , statistics , combinatorics
Recent advances in data mining have integrated kernel functions with Bayesian probabilistic analysis of Gaussian distributions. These machine‐learning approaches can incorporate prior information with new data to calculate probabilistic rather than deterministic values for unknown parameters. This article extensively analyzes a specific Bayesian kernel model that uses a kernel function to calculate a posterior beta distribution that is conjugate to the prior beta distribution. Numerical testing of the beta kernel model on several benchmark datasets reveals that this model's accuracy is comparable with those of the support vector machine (SVM), relevance vector machine, naive Bayes, and logistic regression, and the model runs more quickly than all the other algorithms except for logistic regression. When one class occurs much more frequently than the other class, the beta kernel model often outperforms other strategies to handle imbalanced datasets, including under‐sampling, over‐sampling, and the Synthetic Minority Over‐Sampling Technique. If data arrive sequentially over time, the beta kernel model easily and quickly updates the probability distribution, and this model is more accurate than an incremental SVM algorithm for online learning.