Collaborative decision making by ensemble rule based classification systems

Rule based classification is a popular approach for decision making. It is also achievable that multiple rule based classifiers work together for group deci-sion making by using ensemble learning approach. This kind of expert system is referred to as ensemble rule based classification system by means of a system of systems. In machine learning, an ensemble learning approach is usually adopted in order to improve overall predictive accuracy, which means to provide highly trusted decisions. This chapter introduces basic concepts of ensemble learning and reviews Random Prism to analyze its performance. This chapter also introduces an extended framework of ensemble learning, which is referred to as Collaborative and Compet-itive Random Decision Rules (CCRDR) and includes Information Entropy Based Rule Generation (IEBRG) and original Prism in addition to PrismTCS as base clas-sifiers. This is in order to overcome the identified limitations of Random Prism. Each of the base classifiers mentioned above is also introduced with respects to its essence and applications. An experimental study is undertaken towards comparative validation between the CCRDR and Random Prism. Contributions and Ongoing and future works are also highlighted.