Modelling and Feature Selection with Applications to Big Data Problems

建模和特征选择及其在大数据问题中的应用

• 批准号: RGPIN-2019-05963
• 负责人: Korenberg, Michael
• 金额: $ 1.68万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714939
• 关键词: Modelling; Feature; Selection; Applications; Big

Objectives of proposed research are to continue (1) to develop techniques for system modelling and feature selection, creating methods of extracting useful information applicable to both short noisy experimental records and big data problems, and (2) to apply such methods to important problems in physical and industrial systems. Proposed research will devise methods to detect key features/entities and identify causal relationships by applying Fast Orthogonal Search (FOS) and Modified FOS (MFOS) algorithms. In networks of interacting agents, FOS and MFOS will reverse engineer which key network entities control activities of the others. FOS and MFOS select one specific entity at a time and identify which others are interacting agents. The approach will include machine learning, deep learning, pattern recognition, and classification to determine both the most influential entities and cause-and-effect relationships. The approach will integrate FOS and MFOS into Deep Learning strategies and fuzzy interface systems. One unique aspect of the proposed methodology is deducing interactions of inhibition and activation between entities despite high encryption levels. FOS detects key words in encrypted messages, achieving highest accuracy of any method tested [McGaughey et al, A Systematic Approach of Feature Selection for Encrypted Network Traffic Classification, 2018 Annual IEEE SysCon]. Hence refinement to enable high-speed detection of encrypted words will be part of proposed methodology. Other real--world problems include understanding cardiac arrhythmia. Key to FOS, MFOS and parallel cascade identification (PCI) is ability to search very large candidate sets to rapidly find the best terms to predict the value of some output variable. In a network of suspected terrorists we can identify which entities (individuals or cells) best predict the time activities of other entities (e.g., use of communication devices, internet time, etc). If each time function assigned to a group member is the time that person is using a communication device, then candidate terms to predict one person's time function may involve not only the other group members' time functions but also cross--products thereof. This way other people's activities become apparent without ever demonstrating overt interaction. The proposed approach is unique in its ability to identify even the least obvious candidates those entities never having apparent communication with others but yet best predicting the network activity of other entities. FOS and PCI have been used to successfully reverse engineer gene regulatory networks [Zhen Wang, MSc thesis, School of Computing, Queen's University, October 2010]. However, FOS, MFOS, and PCI have not been applied to detect and disrupt terrorist network activity. We will employ FOS and MFOS in parallel implementation which has proven to be up to 10 times faster than the Fast Fourier Transform, the gold standard in inline coherence imaging in physics.

拟议研究的目标是继续