Dynamic full Bayesian ensemble classifiers for small time series

Abstract

Improving the reliability of small time series classifiers with continuous attributes is an important and challenging task. The information contained in small time series is not sufficient and a temporal dependency exists between data records, which makes it very difficult to optimize the fitting degree between the classifier and the data, and many mature techniques of non-time series data classifiers are not practical. We use a dynamic full Bayesian classifier to increase the amount of information provided by the attribute to the class, and realize the fusion of temporal and nonsequential information. By combining the conditional joint density estimation of attributes based on the multivariate Gaussian kernel function with a diagonal smoothing parameter matrix, the interval division of smoothing parameter values, the timing progressive classification accuracy criterion, the construction of the smoothing parameter configuration tree, classifier selection and averaging, etc, a dynamic full Bayesian ensemble classifier was established for small time series. Experiments were performed using small time series in macroeconomic analysis. The results show that the optimized dynamic full Bayesian ensemble classifiers have very good classification accuracy.