I- Introduction

II- Preliminaries and Notation

III- Design of Scoring Function

IV- Structure Learning

V- Experimental Results

VI- CONCLUSIONS AND FUTURE WORKS

References

Abstract

Bayesian network utilizes graphical model to describe dependencies among variables in probabilistic way, it is one of the most important model for uncertainty processing in Artificial Intelligence. Incremental learning of Bayesian networks has been received more attentions in recent years, in this paper a novel method is proposed to learn Bayesian network from incremental data. In this method, a novel incremental scoring function is designed to adaptively adjust the tendency of matching new and old data in the process of incremental learning. We propose an improved adaptive incremental structure learning algorithm for Bayesian network. Theoretical analysis and experimental results both demonstrate the proposed method outperforms other state-ofthe-art methods.

INTRODUCTION

With the coming of big data era, the statistical machine learning for probabilistic graphical model has attracted extensive attention in recent years. Bayesian network is one of the most typical probabilistic graphical models which is a fundamental model for uncertainty processing in Artificial Intelligence [1]. Learning Bayesian networks from data is NP-hard problem and still one of the most challenges in machine learning [2]. The incremental learning of Bayesian networks is an area that has gained more importance in recent years, in this case, data records are received sequentially, and Bayesian network is constructed incrementally [3]. In this paper, we propose a score-based adaptive algorithm to learn Bayesian network in the presence of concept drift. We design a scoring function which makes the learning process adaptively regulate the searching strategy for each local structure of Bayesian network, then we propose an adaptive parameter learning method based on Lagrange multiplier, we also provide an improved structure learning method. The remainder of this paper is organized as follows: Section II provides the preliminaries and notations of the incremental learning of Bayesian network, and the novel scoring function is proposed in Section III. Section IV offers learning method. Section V offers the experimental results and comparisons of the proposed method. Finally, conclusions are summarized in Section VI.