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

研究启动奖：针对高维、重叠类和不平衡数据的智能优化、聚类和分类框架

• 批准号: 1505509
• 负责人: Nian Zhang
• 金额: $ 20万
• 依托单位: University of the District of Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1505509&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Intelligent; Optimization

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.

研究启动奖为历史悠久的黑人学院和大学的初级和中级教师提供了支持，他们正在构建新的研究计划，或重新指导和重建现有的研究计划。预计该奖项有助于进一步促进教职员工的研究能力和有效性，改善他的家庭机构的研究和教学，并让本科生参与研究经验。 哥伦比亚大学（UDC）颁发的奖项在许多领域都具有更广泛的影响。许多大规模，复杂和网络系统中数据可用性的扩展导致需要从无限的规模和不平衡数据中提高对学习的理解，以支持决策过程。一种有效的不平衡学习系统，为高度重叠的不平衡类，涉及罕见疾病，异常行为甚至痕迹爆炸性的阶级可以节省金钱和人类的生命。该项目从该项目中发展的知识将有助于改进的聚类和分类算法。该项目还将在UDC上增强本科生的研究经验和培训。拟议的研究将开发一种计算廉价的上下文敏感的课堂内聚类方法，通过使用上下文类别数据作为边界来生成非重叠的子群体来克服班级重叠问题。这种新颖的算法可以将任意数据分布分离为非重叠的单峰群集，同时利用中间上下文数据分布进一步分开簇。将开发一种新的基于群体智能的混合全局优化学习模型，以同时优化特征子集和最小方支持向量机的调谐参数。此外，将创建一种新型的粒子群优化算法，以通过从有限代表性和代表性不足的少数群体数据中获得有用的知识来提高分类性能。

项目成果

Weighted Non-negative Matrix Factorization for Image Recovery and Representation

用于图像恢复和表示的加权非负矩阵分解

• 发表时间: 2020
• 期刊: International journal of computational intelligence systems
• 影响因子: 2.9
• 作者: Xiangguang Dai, Nian Zhang

Development of a Drought Prediction System Based on Long Short-Term Memory Networks (LSTM)

基于长短期记忆网络（LSTM）的干旱预测系统的开发

• 发表时间: 2020
• 期刊: 17th International Symposium on Neural Networks (ISNN 2020
• 作者: Nian Zhang, Xiangguang Dai

A neurodynamics-based nonnegative matrix factorization approach based on discrete-time projection neural network

• DOI: 10.1007/s12652-019-01550-5
• 发表时间: 2019-10
• 期刊: J. Ambient Intell. Humaniz. Comput.
• 作者: N. Zhang;Keenan Leatham

Robust Graph Regularized Non-negative Matrix Factorization for Image Clustering

• DOI: 10.1007/978-3-030-64221-1_21
• 发表时间: 2020
• 作者: Xiangguang Dai;Keke Zhang;Juntang Li;Jiang Xiong;Nian Zhang