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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738624
• 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（1）开发用于系统建模和特征选择的技术，创建提取适用于简短嘈杂的实验记录和大数据问题的有用信息的方法，以及（2）将这些方法应用于物理和工业系统中的重要问题。拟议的研究将设计方法来检测关键特征/实体，并通过应用快速正交搜索（FOS）和修改的FOS（MFOS）算法来识别因果关系。在交互代理的网络中，FOS和MFO将反向工程师，关键网络实体控制其他人的活动。 FOS和MFO一次选择一个特定的实体，并确定其他实体正在相互作用。该方法将包括机器学习，深度学习，模式识别和分类，以确定最有影响力的实体和因果关系。该方法将将FOS和MFO集成到深度学习策略和模糊接口系统中。提出的方法的一个独特的方面是，尽管加密水平很高，但仍推论抑制和激活之间的相互作用。 FOS检测到加密消息中的关键单词，可以实现任何测试方法的最高精度[McGaughey等人，这是一种系统选择加密网络流量分类的功能选择方法，2018年IEEE SYSCON年度IEEE SYSCON]。因此，改进以实现加密单词的高速检测将是拟议方法论的一部分。其他真实的问题包括了解心律不齐。 FOS，MFO和并行级联识别（PCI）的关键是能够搜索非常大的候选设置以快速找到最佳术语以预测某些输出变量的值。在一个可疑的恐怖分子网络中，我们可以确定哪些实体（个人或细胞）最好预测其他实体的时间活动（例如，使用通信设备，互联网时间等）。如果分配给组成员的每个时间函数是人使用通信设备的时间，那么预测一个人的时间函数的候选术语不仅涉及其他小组成员的时间函数，而且还涉及其越野函数。这样一来，别人的活动就变得显而易见，而没有展示明显的互动。提出的方法在识别最不明显的候选人的能力上是独一无二的 - 这些实体从未与他人进行明显的沟通，但可以最好地预测其他实体的网络活动。 FOS和PCI已被用于成功逆转工程师基因调节网络[Zhen Wang，MSC论文，计算学院，皇后大学，2010年10月]。但是，FOS，MFO和PCI尚未用于检测和破坏恐怖网络活动。我们将在并行实施中采用FOS和MFO，事实证明，该实施的速度比快速傅立叶变换快10倍，即物理学中的内联相干成像中的金标准。