Cooperative Machine Learning with Information Fusion for Dynamic Decision Making in Diagnostic Applications

In many applications, use of all relevant data to extract more information from multiple sources of information and achieve higher accuracy in prediction is desirable. Cooperative learning is observed in human and some animal societies. Sound knowledge and information acquisition, cooperation in learning amongst multi-agent systems may result in a higher effectiveness compared to individual learning. Cooperative learning in multi agent systems is generally expected to improve both quality & speed of learning. According to survey maximum research papers focus on coordinated approach of agents. Multiple sources of data can be viewed as different, related views of the same learning problem, where dependencies between the views could potentially take on complex structures. This gives rise to interesting and challenging machine learning problems where data sources are combined for learning. This framework encompasses several data fusion tasks and related topics, such as transfer learning, multitask learning, multiview learning, and learning under covariate shift. The advantages of the multiple source learning paradigm is seen in situations where individual data sources are noisy, incomplete, and learning from more than one source can filter out problem-independent noise. Cooperative learning is an approach where one or more team of learners work together towards reaching a better understanding of a specified task. The purpose of this paper is to use this approach to describe a proposal for designing and building a cooperative machine learning system (Multi-Learning system) that contains two or more machine learners that cooperate together.