A new online learning algorithm for streaming data and decision support with a Bayesian approach

ABSTRACT

With the new revolution in data technology, many types of streaming data are automatically generated in our living environment. The vast amount of information carried by this streaming data demands decision support from new online learning algorithms. In this paper, we propose a new online learning algorithm to monitor changes in stream data/system status and provide automatic decision support using streaming data. The new online learning algorithm we propose consists of two components. Firstly, we adapt an existing Bayesian approach into an online learning algorithm for change detection, which serves as Component A. Then, we integrate this algorithm with a modified online learning method from repeated games (Play against Random Past), which forms Component B. Theoretical support for Component B is provided with a mathematical proof at the end. We demonstrate the algorithm's performance in this paper through simulations using both artificial data from a random process and data from the 2009 H1N1 pandemic.

KEYWORDS:

• Change point detection
• automatic decision-making
• Bayesian approaches
• online learning
• artificial intelligent

Disclosure statement

No potential conflict of interest was reported by the author(s).