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

研究启动奖为历史悠久的黑人学院和大学的初级和职业生涯中期教师提供支持，这些教师正在建立新的研究计划或重新定向和重建现有的研究计划。预计该奖项有助于进一步提高教师的研究能力和有效性，改善其所在机构的研究和教学，并让本科生参与研究经验。授予哥伦比亚特区大学(UDC)的奖项可能在许多领域产生更广泛的影响。在许多大规模、复杂和联网的系统中，数据可用性的扩展导致需要提高对从无限大小和不平衡数据中学习的理解，以支持决策过程。为涉及罕见疾病、异常行为甚至微量爆炸物的高度重叠的不平衡类开发有效的不平衡学习系统，可以节省资金和生命。通过该项目开发的知识将有助于改进集群和分类算法。该项目还将加强UDC本科生的研究经验和培训。拟议的研究将开发一种计算成本较低的上下文敏感类内聚类方法，通过使用上下文类数据作为边界生成不重叠的子聚类来克服类重叠问题。这种新的算法可以将任意数据分布分离成不重叠的单峰聚类，同时利用介入上下文数据分布来进一步分离聚类。为了同时优化最小二乘支持向量机的特征子集和调整参数，提出了一种基于群体智能的混合全局优化学习模型。此外，还将创建一种新的粒子群优化自组织算法，通过从有限且未被充分代表的少数类数据中获取有用知识来提高分类性能。

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.