Research Initiation Award: An Intelligent Optimization, Clustering and Classification Framework for High Dimensional, Overlapped Classes, and Imbalanced Data

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Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at his home institution, and involves undergraduate students in research experiences. The award to the University of the District of Columbia (UDC) has potential broader impact in a number of areas. The expansion of data availability in many large-scale, complex, and networked systems leads to a need to advance the understanding of learning from unbounded size and imbalanced data to support decision-making processes. An effective imbalanced learning system developed for the highly overlapped imbalanced classes involving rare diseases, abnormal behavior, or even trace explosive, can save money and human life. The knowledge developed from this project will contribute to improved clustering and classification algorithms. This project will also enhance the research experience and training of undergraduate students at UDC.The proposed research will develop a computationally cheap context-sensitive intra-class clustering approach to overcome class overlapping problems by generating non-overlapping sub-clusters using context class data as a boundary. This novel algorithm can separate an arbitrary data distribution into non-overlapping unimodal clusters, while utilizing intervening context data distributions to further separate the clusters. A new swarm intelligence-based hybrid global optimization learning model will be developed to simultaneously optimize the feature subset and the tuning parameters of the least square support vector machine. Moreover, a novel particle swarm optimization self-organizing algorithm will be created to improve the classification performance through obtaining useful knowledge from the limited and underrepresented minority class data.