A0801
Title: Approximate Bayesian classifier for high-dimensional data
Authors: Myungjin Kim - Kyungpook National University (Korea, South)
Gyuhyeong Goh - Kyungpook National University (Korea, South)
Yongku Kim - Kyungpook National University (Korea, South)
Jieun Lee - Kyungpook National University (Korea, South)
Jieun Lee - Kyungpook National University (Korea, South) [presenting]
Abstract: The Bayes classifier provides a way of performing probabilistic classification using a posterior distribution of the outcome given predictors. When there are many predictors, however, the need for estimating the high-dimensional covariance matrix, which yields prohibitively high computational cost, makes its applicability limited. A naive Bayes classifier is a popular alternative for handling high-dimensional data since its assumption on the conditional independence of features given class eases the burden associated with the high-dimensional covariance matrix. Despite its computational efficiency, a naive Bayes classifier leads to poor statistical performance when the features are correlated, a case commonly observed in real-world data. To address such an issue, a new Bayesian classifier is proposed, called the approximate Bayesian classifier. The method is based on the Vecchia approximation that has played a crucial role in dimension reduction in recent Bayesian spatial modeling. To adapt the Vecchia approach for spatial modeling into a classification framework, the concept of neighborhood is defined, which lies at the core of the Vecchia approximation, using the relationship between the coefficients and the correlations under the normality assumption. The performance of the proposed method is investigated via numerical studies.
