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Group Penalized Multinomial Logit Models and Stock Return Direction Prediction

IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第6期70卷 4297-4318页

作者： Hu, Xuemei Yang, Junwen Chongqing Technol & Business Univ Chongqing Key Lab Stat Intelligent Comp & Monitori Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Sch Math & Stat Chongqing 400067 Peoples R China

Multinomial logit model(MLM) has been proposed as the most frequently regression model for multi-category response. To deal with correlated data, in this paper we propose G-LASSO/G-SCAD/G-MCP penalized MLM to exert class discovery and class prediction for high-dimensional multi-category classification problems. Firstly, we develop a group coordinate descent(GCD) algorithm to simultaneously complete group selection and group estimation, and prove the convergence to the proposed GCD algorithm under mild conditions. Secondly, we apply the training set and group estimations to obtain class probability estimators, choose the Bayes classifier to identify class index information, and introduce the testing set and a few measures to assess multi-category prediction performance. Simulations show that the proposed methods outperform LASSO/SCAD/MCP penalized MLM, 3 deep learning methods and 3 machine learning methods in terms of Kappa, PDI(polytomous discrimination index), Optimal or Average Accuracy. Finally, we combine group penalized MLM with 58 technical indicators to predict up trends, sideways trends and down trends for stock returns, and show that the proposed methods outperform the other 9 methods in terms of Accuracy, PDI, Kappa and HUM. Therefore, the proposed method can not only accommodate the correlation information, but also improve multi-category prediction performance by shrinking group coefficients.

关键词： Prediction algorithms Logistic regression Vectors Convergence Market research Optimization machine learning algorithms Deep learning models gCD algorithm group estimations machine learning models multi-category prediction performance

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Critical Analysis of Cross-Validation Methods and Their Impact on Neural Networks Performance Inflation in Electroencephalography Analysis

IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING

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IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2021年第1期44卷 75-82页

作者： Abdulaal, Mohammed J. Casson, Alexandre J. Gaydecki, Patrick King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21441 Saudi Arabia Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England

The performance of a brainx2013;computer interface (BCI) system is usually measured by its classification accuracy. This creates motivation to increase system accuracies. This article investigates the variation of accuracy values in emotion recognition studies and their relation to cross-validation methods. The literature shows values of accuracies ranging from 60x0025;to 99x0025;while using similar classifiers when tested on the same data set. The study included a literature review of 65 articles testing their algorithms on the DEAP data set up until 2019. Moreover, the study involved the reimplementation of a neural network classifier in both nested and nonnested cross-validation methods. The results show accuracies up to 90x0025;when using nonnested cross validation, while nested cross-validation achieves accuracies around 60x0025;. This article aims to motivate researchers to clearly describe their cross-validation method to avoid confusing other researchers when benchmarking their algorithms.

关键词： Emotion recognition Artificial neural networks Robustness Electroencephalography Distance measurement Classification algorithms Biological neural networks Brain-computer interface (BCI) emotion recognition machine learning algorithms neural networks (NNs)

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Recursive Variable Projection Algorithm for a Class of Separable Nonlinear Models

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2021年第11期32卷 4971-4982页

作者： Gan, Min Guan, Yu Chen, Guang-Yong Chen, C. L. Philip Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China Univ Macau Fac Sci & Technol Macau Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China

In this article, we study the recursive algorithms for a class of separable nonlinear models (SNLMs) in which the parameters can be partitioned into a linear part and a nonlinear part. Such models are very common in machine learning, system identification, and signal processing. Utilizing the special structure of the SNLMs, we propose a recursive variable projection (RVP) algorithm, in which at each recursion, the linear parameters of the model are eliminated, and the nonlinear parameters are updated by the recursive Levenberg-Marquart algorithm. Then, based on the updated nonlinear parameters, the linear parameters are updated by the recursive least-squares algorithm. According to a convergence analysis of the RVP algorithm, the parameter estimation error is mean-square bounded. Numerical examples confirm the satisfactory performance of the proposed algorithm.

关键词： Signal processing algorithms Adaptation models Gallium nitride Computer science machine learning algorithms Numerical models Convergence Online learning recursive identification separable nonlinear models (SNLMs) variable projection (VP)

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Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on machine Vision

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IEEE ACCESS 2023年 11卷 106722-106732页

作者： Li, Tong Zheng, Jiang Xihua Univ Sch Mat Sci & Engn Chengdu 610039 Peoples R China

The welding process for medium and thick plates typically involves multi-layering and multi-channeling, but its quality and reliability require further improvement. Therefore, this study introduces the convolution neural network algorithm to establish a deep learning model for weld seam recognition. Additionally, the structured light imaging method is used to accurately position the V-shaped weld groove. Meanwhile, a machine vision based multi-layer and multi-pass dynamic routing planning algorithm was also studied and designed, and a contour feature point recognition algorithm for the filling layer was developed. Thus, dynamic routing planning is achieved. It is demonstrated that the difference between the coordinates acquired by the deep learning model and the ideal region decreases steadily and reaches a minimum of (200,80). The confidence level of weld seam detection gradually increases with the adjustment of the welding robot, reaching a maximum of 98%. The confidence level of the detected feature points reaches 100%. In the meantime, the remaining height of the fusion after welding is 2.5mm. There are no negative phenomena present on the surface of the weld seam, meeting the necessary process requirements. Such discrepancies as undercut, incomplete penetration, slag inclusion, and porosity are absent. It shows that the welding technology based on machine vision has strong feasibility, effectively improves the automation level and efficiency of welding technology, and provides reliable technical support for the development of modern machine vision welding technology.

关键词： machine learning multiple layers and channels initial point positioning dynamic lane arrangement welding machine learning algorithms robot vision systems

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A BCI System for Imagined Speech Classification Based on Optimization Theory

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第4期70卷 6679-6690页

作者： Zheng, Xiao-Ben Ling, Bingo Wing-Kuen Guangdong Univ Technol Sch Informat Engn Guangzho 510006 Peoples R China

Electroencephalograms (EEGs) are used for establishing a connection between the human brain and the outside environment, so they are widely used in the brain computer interface (BCI). Nowadays, the imagined speech (IS) is a highly promising paradigm of the BCI. It can be used for controlling the external devices directly. However, the features for performing the IS are unknown. Hence, the numerous features are extracted. As a result, the dimension of the feature vectors is extremely large. To reduce the required computation, the clustering is required to be performed in the low dimensional space. Under this circumstance, the transform matrix affects both the dimensional reduction part and the clustering part. In fact, finding the transform matrix and the clustering centers under this scenario is challenging. To tackle this difficulty, this paper provides a modified joint principal component analysis (PCA) and k means algorithm for performing the IS. Here, the interclass separation among the feature vectors is also taken into an account of the problem formulation. In particular, the problem is formulated as a nonconvex constrained optimization problem. The total two norm reconstruction error of the feature vectors as well as the total two norm differences between the feature vectors and the clustering centers in the low dimensional space and the total two norm differences among the clustering centers are minimized subject to the orthogonality of the transform matrix. The numerical computer simulations are conducted based on the multi-class IS classification database. The obtained results show that our proposed method outperforms the various states of the art methods in terms of the clustering accuracy and the average required execution time. Overall, using the BCI system for performing the imagined speech classification plays an important role in the consumer electronics area.

关键词： Clustering algorithms Optimization Consumer electronics Transforms Principal component analysis machine learning algorithms Classification algorithms Vectors Electroencephalography Dimensionality reduction Semi-supervised imagined speech k means clustering in the low dimensional space principal component analysis linear discriminant analysis nonconvex constrained optimization problem

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The Roots of Bioinformatics in ISMB

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PLOS COMPUTATIONAL BIOLOGY 2012年第8期8卷 e1002679-e1002679页

作者： Gibson, Todd A. Calif State Univ Chico Dept Comp Sci Chico CA 95929 USA

Intelligent Systems for Molecular Biology (ISMB). Since its inception, ISMB has held meetings on four continents and published proceedings papers from researchers in 34 different countries. [...]the United States National Institutes of Health (NIH) was in a political battle with the Department of Energy (DoE) over the Human Genome Project (which began in October of 1990). Though today nobody would question the province of NIH to control the Human Genome Project, at the time GenBank was run by the DoE's Los Alamos National Laboratory, and the DoE had experience breaking up genomes using radiation hybrids.

关键词： Artificial intelligence Computational biology Computer and information sciences Molecular biology Biologists Bioinformatics machine learning algorithms Scientists

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Can a machine Learn Through Customer Sentiment? A Cost-Aware Approach to Predict Support Ticket Escalations

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IEEE SOFTWARE 2019年第5期36卷 38-45页

作者： Werner, Colin Li, Ze Shi Damian, Daniela Univ Victoria Victoria BC Canada Univ Victoria Researching Privacy Nonfunct Requirements & Conti Victoria BC Canada Univ Victoria Dept Comp Sci Software Engn Global InterAct Lab Victoria BC Canada

Given the connection between customer happiness and support ticket escalation, we describe an approach that 1) analyzes the emotions in conversations between a customer and a support analyst and 2) provides organizations with a cost-based mechanism to evaluate machine-learning algorithms trained on emotion-related features to predict support ticket escalations.

关键词： Customer Services learning Artificial Intelligence Customer Sentiment Cost Aware Approach Customer Happiness Cost Based Mechanism machine learning algorithms Support Ticket Escalations Emotion Related Features Training Data Software Engineering Sentiment Analysis machine learning algorithms Data Mining Emotion Recognition Sentiment Analysis Natural Language Affective Computing machine learning Modeling And Prediction Cost Sensitive learning Data Mining Defect Escalation machine learning Software Defect Escalation Prediction

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Navigating and Addressing Public Concerns in AI: Insights From Social Media Analytics and Delphi

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IEEE ACCESS 2024年 12卷 126043-126062页

作者： Maghsoudi, Mehrdad Mohammadi, Amirmahdi Habibipour, Sajjad Shahid Beheshti Univ Fac Management & Accounting Dept Ind & Informat Management Tehran *** Iran Iran Univ Sci & Technol Sch Management Econ & Progress Engn Tehran *** Iran KN Toosi Univ Technol Fac Mech Engn Tehran *** Iran

The rapid advancement and integration of artificial intelligence (AI) in various domains of society have given rise to a complex landscape of public concerns. This research endeavors to systematically explore these concerns by employing a multi-stage methodology that combines large-scale social media data collection from Twitter and advanced text analytics. The study identifies seven distinct clusters of concerns, encompassing privacy and security, workforce displacement, existential risks, social and ethical implications, dependency on AI, misuse of AI, and lack of transparency. To further contextualize these findings, the Delphi method was employed to gather insights from AI ethics experts, providing a deeper understanding of the public's apprehensions. The results underscore the critical need for addressing these concerns to foster public trust and acceptance of AI technologies. This comprehensive analysis offers valuable guidance for policymakers, AI developers, and stakeholders to navigate and mitigate the multifaceted issues associated with AI, ultimately contributing to more informed and responsible AI deployment. By addressing these public concerns, the study aims to pave the way for a more ethically sound and socially acceptable integration of AI into society, ensuring that the benefits of AI can be realized while minimizing potential risks and negative impacts. Through this systematic approach, the research highlights the importance of continuous monitoring and proactive management of AI-related concerns to sustain public confidence and promote beneficial AI innovation.

关键词： Artificial intelligence Ethics Social networking (online) Medical services Stakeholders Privacy machine learning algorithms AI concerns social media analysis AI governance responsible AI

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Distributed Random Reshuffling Over Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 1143-1158页

作者： Huang, Kun Li, Xiao Milzarek, Andre Pu, Shi Qiu, Junwen Chinese Univ Hong Kong Shenzhen Res Inst Big Data Sch Data Sci Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518129 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China

In this paper, we consider distributed optimization problems where n agents, each possessing a local cost function, collaboratively minimize the average of the local cost functions over a connected network. To solve the problem, we propose a distributed random reshuffling (D-RR) algorithm that invokes the random reshuffling (RR) update in each agent. We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions. In particular, for smooth strongly convex objective functions, D-RR achieves O(1/T-2) rate of convergence (where T counts the epoch number) in terms of the squared distance between the iterate and the global minimizer. When the objective function is assumed to be smooth nonconvex, we show that D-RR drives the squared norm of the gradient to 0 at a rate of O(1/T-2/3). These convergence results match those of centralized RR (up to constant factors) and outperform the distributed stochastic gradient descent (DSGD) algorithm if we run a relatively large number of epochs. Finally, we conduct a set of numerical experiments to illustrate the efficiency of the proposed D-RR method on both strongly convex and nonconvex distributed optimization problems.

关键词： Convergence Linear programming Signal processing algorithms Gradient methods Distributed databases Big Data machine learning algorithms Distributed optimization random reshuffling stochastic gradient methods

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Linear and Nonlinear Fault Location in Smart Distribution Network Under Line Parameter Uncertainty

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第12期17卷 8308-8318页

作者： Mirshekali, Hamid Dashti, Rahman Shaker, Hamid Reza Samsami, Reza Torabi, Amin Jahromi Persian Gulf Univ Clin Lab Ctr Power Syst & Protect Fac Intelligent Syst Engn & Data Sci Bushehr *** Iran Persian Gulf Univ Engn Coll Elect Engn Grp Bushehr *** Iran Univ Southern Denmark Ctr Energy Informat DK-5230 Odense Denmark

The line parameters of the distribution network (DN) may change because of the atmospheric, structural, and operational conditions. The uncertainty of line parameters can compromise the accuracy of the automatic fault location methods. Besides, arc faults (AFs) may happen in the DN. These faults are difficult to locate in the faulty section because of the nonlinear, asymmetric, and random nature of AF current. In this article, we present a new time-domain fault location (TDFL) method to determine the location of the fault in smart power DN under the line parameters uncertainty. In the suggested method, line parameters' accurate values are determined using a mixed gradient descent particle swarm optimization algorithm. The suggested method's performance is investigated with the help of an IEEE 123-node test feeder in MATLAB (R2018b). The effects of parameter uncertainty, distributed generations operation conditions and modes, different types of AF, various fault distances, resistances, and the fault inception angles are studied. For further evaluation of the proposed method's robustness, two practical tests in the laboratory are carried out. The results confirm that the proposed TDFL method has high accuracy.

关键词： Fault location Time-domain analysis Mathematical model Distribution networks Uncertain systems machine learning algorithms State estimation Distributed generations (DGs) operation modes gradient descent (GD) method optimization problem time-domain fault location (TDFL) method

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