AbstractThis thesis introduces a unified framework for design of rule based systems for classification tasks, which consists of the operations of rule generation, rule simplification and rule representation. This thesis also stresses the importance of combination of different rule learning algorithms through ensemble learning approaches.
For the three operations mentioned above, novel approaches are developed and validated by comparing with existing ones for advancing the performance of using this framework. In particular, for rule generation, Information Entropy Based Rule Generation is developed and validated through comparing with Prism. For rule simplification, Jmid-pruning is developed and validated through comparing with J-pruning and Jmax-pruning. For rule representation, rule based network is developed and validated through comparing with decision tree and linear list. The results show that the novel approaches complement well the existing ones in terms of accuracy, efficiency and interpretability.
On the other hand, this thesis introduces ensemble learning approaches that involve collaborations in training or testing stage through combination of learning algorithms or models. In particular, the novel framework Collaborative and Competitive Random Decision Rules is created and validated through comparing with Random Prisms. This thesis also introduces the other novel framework Collaborative Rule Generation which involves collaborations in training stage through combination of multiple learning algorithms. This framework is validated through comparing with each individual algorithm. In addition, this thesis shows that the above two frameworks can be combined as a hybrid ensemble learning framework toward advancing overall performance of classification. This hybrid framework is validated through comparing with Random Forests.
Finally, this thesis summarises the research contributions in terms of theoretical significance, practical importance, methodological impact and philosophical aspects. In particular, theoretical significance includes creation of the framework for design of rule based systems and development of novel approaches relating to rule based classification. Practical importance shows the usefulness in knowledge discovery and predictive modelling and the independency in application domains and platforms. Methodological impact shows the advances in generation, simplification and representation of rules. Philosophical aspects include the novel understanding of data mining and machine learning in the context of human research and learning, and the inspiration from information theory, system theory and control theory toward methodological innovations. On the basis of the completed work, this thesis provides suggestions regarding further directions toward advancing this research area.
|Date of Award||Oct 2015|
|Supervisor||Alexander Gegov (Supervisor), Ella Haig (Supervisor) & Mohamed Bader-El-Den (Supervisor)|