Modelling and Feature Selection with Applications to Big Data Problems

拟议研究的目标是继续 *（1）开发系统建模和特征选择技术，创建提取适用于短噪声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）和改进的FOS（MFOS）算法来识别因果关系。在交互代理的网络中，FOS和MFOS将反向工程哪些关键网络实体控制其他实体的活动。FOS和MFOS每次选择一个特定的实体，并识别哪些其他实体是相互作用的代理。该方法将包括机器学习、深度学习、模式识别和分类，以确定最具影响力的实体和因果关系。该方法将FOS和MFOS集成到深度学习策略和模糊接口系统中。所提出的方法的一个独特的方面是推断实体之间的抑制和激活的相互作用，尽管高加密级别。FOS检测加密消息中的关键字，实现了任何测试方法的最高精度[McGaughey et al，A Systematic Approach of Feature Selection for Encrypted Network Traffic Classification，2018 Annual IEEE SysCon]。因此，使加密字的高速检测的改进将是所提出的方法的一部分。其他真实的-世界问题包括了解心律失常。FOS、MFOS和并行级联识别（PCI）的关键是能够搜索非常大的候选集，以快速找到预测某些输出变量值的最佳项。在可疑恐怖分子的网络中，我们可以识别哪些实体（个人或细胞）最能预测其他实体的时间活动（例如，通信设备的使用、因特网时间等）。如果分配给组成员的每个时间函数是该人正在使用通信设备的时间，则预测一个人的时间函数的候选项可能不仅涉及其他组成员的时间函数，而且还涉及其叉积。通过这种方式，其他人的活动变得明显，而无需展示公开的互动。所提出的方法是独特的，它能够识别即使是最不明显的候选人，这些实体从来没有明显的通信与他人，但最好的预测网络活动的其他实体。FOS和PCI已被用于成功地对基因调控网络进行逆向工程[Zhen Wang，MSc thesis，School of Computing，Queen's University，October 2010]。然而，FOS、MFOS和PCI尚未被应用于检测和破坏恐怖主义网络活动。我们将采用FOS和MFOS并行实现，这已被证明是高达10倍的速度比快速傅立叶变换，在物理学的同轴相干成像的黄金标准。

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.