A Bayesian beta kernel model for binary classification and online learning problems
MacKenzie, Cameron A.
Trafalis, Theodore B.
MetadataShow full item record
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 paper extensively analyzes a speci c 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 data sets reveals that this model's accuracy is comparable with those of the support vector machine, relevance vector machine, naive Bayes, and logistic regression, and the model runs more quickly than other algorithms. When one class occurs much more frequently than the other class, the beta kernel model often outperforms other strategies to handle imbalanced data sets, including undersampling, 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 support vector machine algorithm for online learning.
Statistical Analysis and Data Mining, 7(6), 434-449. Author's accepted manuscriptThe article of record may be found at http://dx.doi.org/10.1002/sam.11241
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
Showing items related by title, author, creator and subject.
The instrumentation of a kernel DBMS for the execution of kernel transactions equivalent to their object-oriented transactions Clark, Robert Eugene; Yildirim, Necmi (Monterey, California. Naval Postgraduate School, 1995-09);The issues addressed in this thesis are to examine whether the data manipulation operations of the kernel database system are capable of supporting the new Object-Oriented Data Model and Language Interface (OODM&L Interface). ...
Senocak, Erhan. (Monterey, California. Naval Postgraduate School, 1995-12);In a stand-alone database management system (DBMS), one of the key components is the real time monitor (RTM) which handles database accesses and responses at run time. In the Multimodel, Multilingual and Multibackend ...
Parker, Robert Earl, Jr. (Monterey, California. Naval Postgraduate School, 1992-09);Estimating the spectra of non-stationary signals represents a difficult challenge. Classical techniques employing the Fourier transform and local stationarity have been employed with limited success. A more promising ...