FMDBN: A first-order Markov dynamic Bayesian network classifier with continuous attributes

With the development of data driven decision making and prediction, time-series data are ubiquitous and the demand for its classification is vast. Although a large body of research has been reported in the literature, it is mainly oriented to situations in which class and attributes are changing simultaneously. In practice however, those class and attributes changes are not always synchronous. This means that further studies for asynchronous classifier problems are necessary. In this paper, a first-order Markov dynamic Bayesian network classifier is proposed to address the asynchronous issue, by combing time-series data preprocessing, time-delayed and dislocated transformation of variables, initial and evolutionary learning. The attribute density in this classifier is estimated based on Gaussian function, and the classification accuracy criterion for time-series progressiveness is also considered. This classifier has a relatively simple structure, which can avoid the problem of overfitting. In addition, data can effectively be classified by utilizing three kinds of classification information, namely time-delayed, non-time-delayed and mixed information in multivariate time-series datasets. The proposed method is also able to accumulate classification information via iterative evolution and thus improve the generalization of classifiers. Experiments were carried out by using standard time-series datasets from UCI, financial and macroeconomic domains. The experimental results show that the proposed first-order Markov dynamic Bayesian network classifier is more accurate in dealing with these dynamic classification problems.