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Depression Level classification Using Machine Learning Classifiers Based on Actigraphy Data

IEEE ACCESS 2021年 9卷 116622-116646页

作者： Choi, Jung-Gu Ko, Inhwan Han, Sanghoon Yonsei Univ Yonsei Grad Program Cognit Sci Seoul 03722 South Korea Yonsei Univ Dept Psychol Seoul 03722 South Korea

Estimating and classifying depression status are critical in the clinical and psychological domains to map the course of treatment. Prior researchers used biosignal time-series data to reflect the variation in factors associated with depression. In addition, machine learning algorithms were applied to determine the underlying relationships between depressive symptoms and these factors. In this study, we introduce a classification framework for depression levels using actigraphy data based on machine learning algorithms. Fourteen circadian rhythm features (minimum, amplitude, alpha, beta, acrotime, upmesor, downmesor, mesor, f_pseudo, interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), M10, and L5) extracted from accelerometer-based actigraphy data were used to model depression status with survey variables. Six evaluation metrics (accuracy, precision, recall, F1-score, receiver operating characteristic curve, and area under the curve) were applied to validate the performance of the proposed framework. Among the four candidate classifiers (XGBoost classifier, support vector classifier, multilayer perceptron, and logistic regression), the XGBoost classifier was the best at classifying depression levels. Moreover, we confirmed that the actigraphy data of two days were optimal for feature extraction and classification. The results of this study provide novel insights into the relationship between depression and physical activity in terms of both identification of depression and application of actigraphy data.

关键词： Depression Feature extraction Data mining classification algorithms Circadian rhythm Licenses Indexes Accelerometer-based actigraphy data classification algorithm circadian rhythm depression level machine learning multi-level classification physical activity

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Fully online classification by regularization

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APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2007年第2期23卷 198-214页

作者： Ye, Gui-Bo Zhou, Ding-Xuan Fudan Univ Sch Math Sci Shanghai 200433 Peoples R China City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China

In this paper we consider fully online learning algorithms for classification generated from Tikhonov regularization schemes associated with general convex loss functions and reproducing kernel Hilbert spaces. For such a fully online algorithm, the regularization parameter in each learning step changes. This is the essential difference from the partially online algorithm which uses a fixed regularization parameter. We first present a novel approach to the drift error incurred by the change of the regularization parameter. Then we estimate the error of the learning process for the strong approximation in the reproducing kernel Hilbert space. Finally, learning rates are derived from decays of the regularization error. The convexity of the loss function plays an important role in our analysis. Concrete learning rates are given for the hinge loss and the support vector machine q-norm loss. (c) 2007 Elsevier Inc. All rights reserved.

关键词： classification algorithm online learning reproducing kernel Hilbert spaces regularization error analysis

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AUTOMATIC VEHICLE classification IN SYSTEMS WITH SINGLE INDUCTIVE LOOP DETECTOR

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METROLOGY AND MEASUREMENT SYSTEMS 2014年第4期21卷 619-630页

作者： Gajda, J. Mielczarek, M. AGH Univ Sci & Technol Dept Measurement & Elect PL-30059 Krakow Poland

The work proposes a new method for vehicle classification, which allows treating vehicles uniformly at the stage of defining the vehicle classes, as well as during the classification itself and the assessment of its correctness. The sole source of information about a vehicle is its magnetic signature normalised with respect to the amplitude and duration. The proposed method allows defining a large number (even several thousand) of classes comprising vehicles whose magnetic signatures are similar according to the assumed criterion with precisely determined degree of similarity. The decision about the degree of similarity and, consequently, about the number of classes, is taken by a user depending on the classification purpose. An additional advantage of the proposed solution is the automated defining of vehicle classes for the given degree of similarity between signatures determined by a user. Thus the human factor, which plays a significant role in currently used methods, has been removed from the classification process at the stage of defining vehicle classes. The efficiency of the proposed approach to the vehicle classification problem was demonstrated on the basis of a large set of experimental data.

关键词： inductive loop detector magnetic signature vehicle classification classification algorithm

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Point Target classification via Fast Lossless and Sufficient Ω-Ψ-Φ Invariant Decomposition of High-Resolution and Fully Polarimetric SAR/ISAR Data

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PROCEEDINGS OF THE IEEE 2013年第3期101卷 798-830页

作者： Paladini, Riccardo Famil, Laurent Ferro Pottier, Eric Martorella, Marco Berizzi, Fabrizio Mese, Enzo Dalle Univ Pisa Dept Informat Engn I-56126 Pisa Italy Univ Rennes 1 IETR F-35000 Rennes France

The classification of high-resolution and fully polarimetric SAR/ISAR data has gained a lot of attention in remote sensing and surveillance problems and is addressed by decomposing the radar target Sinclair matrix. In this paper, the Sinclair matrix has been projected onto the circular polarization basis and is decomposed into five parameters that are invariant to the relative phase Phi, the Faraday rotation Omega, and the target orientation Psi without any information loss. The physical interpretation of these parameters, useful for target classification studies, is found in the wave-particle nature of radar scattering phenomenon given the circular polarization of elemental packets of energy. The proposed deterministic target decomposition is based on the left-orthogonal special unitary SU(2) basis, decomposing the signal backscattered by point targets, represented by the target vector, via six special unitary SU(4) rotation matrices, and by providing full resolution and lossless analysis. Comparisons between the proposed deterministic target decomposition and the Cameron, Kennaugh, Krogager, and Touzi decompositions are also pointed out. Generally, the proposed decomposition provides simpler interpretation, faster parameter extraction, and better generalization properties for the analysis of nonreciprocal or random targets. Several polarimetric SAR/ISAR data sets of UWB data, airborne fully polarimetric EMISAR data, and spaceborne RADARSAT2 are used for illustrating the effectiveness and the usefulness of this decomposition for the classification of point targets. Results are very promising for application use in the next generation of high-resolution spaceborne and airborne Pol-SAR and Pol-ISAR systems.

关键词： Automatic target classification automatic target recognition classification algorithm data mining decomposition theorem depolarization effect deterministic processes Earth surface eigenvalues and eigenfunctions Einstein photon circular polarization Faraday rotation geophysics computing invariant decomposition lunar surface matrix decomposition orientation invariant parameters particle characterization of radio scattering theory polarimetry polarization polarization transformation properties radar radar cross section (RCS) radar polarimetry radio scattering models remote sensing by radar Sinclair matrix synthetic aperture radar target decomposition target scattering characterization vectors

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In-Vehicle Network Injection Attacks Detection Based on Feature Selection and classification

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Automotive Innovation 2024年第1期7卷 138-149页

作者： Haojie Ji Liyong Wang Hongmao Qin Yinghui Wang Junjie Zhang Biao Chen Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100192China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Hefei Innovation Research Institute Beihang UniversityHefei 230012China

Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle *** transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols ***,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal ***,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network *** this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection *** consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is *** addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal *** paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data *** experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.

关键词： classification algorithm Anomaly detection In-vehicle network Feature extraction Injecting attack

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Machine Learning classification algorithms for Phishing Detection: A Comparative Appraisal and Analysis

Machine Learning Classification Algorithms for Phishing Dete...

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International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf)

作者： Noah Ndakotsu Gana Shafi’I Muhammad Abdulhamid Department of Cyber Security Science Federal University of Technology Minna Nigeria

ISBN: (纸本)9781728107141

Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy has been a setback in phishing detection. In this research RandomForest, SysFor, SPAARC, RepTree, RandomTree, LMT, ForestPA, JRip, PART, NNge, OneR, AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, LibSVM, and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms were rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quit a number of classifiers also exist which if properly explored will yield more accurate results for phishing detection. RondomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false positive rate for phishing classification which suggest that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.

关键词： Phishing attack classification algorithm Accuracy Performance metric RandomForest spear phishing Sorting algorithm phishing computer information security False-positives Classifiers Performance metrics application developer quadratic errors

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classification of Strongly Conflict-Avoiding Codes

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IEEE COMMUNICATIONS LETTERS 2018年第12期22卷 2415-2418页

作者： Baicheva, Tsonka Stefanova Topalova, Svetlana Todorova Bulgarian Acad Sci Inst Math & Informat Veliko Tarnovo 5000 Bulgaria DA Tsenov Acad Econ Svishtov 5250 Bulgaria

Strongly conflict-avoiding codes (SCACs) for slot-asynchronous multiple-access collision channels without feedback are considered. Codes with maximum size (optimal codes) for given parameters are of interest. In this letter, we present classification results about optimal SCACs of weights 3, 4, and 5 and given small lengths. We also determine some previously unknown values of the size of optimal SCACs with these parameters.

关键词： Asynchronous communication strongly conflict-avoiding codes classification algorithm

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Unsupervised classification for hybrid polarimetric SAR data based on scattering mechanisms and Wishart classifier

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ELECTRONICS LETTERS 2015年第19期51卷 1530-1531页

作者： Chen, Shiqiang Guo, Shenglong Li, Yang Hong, Wen Chinese Acad Sci Inst Elect Natl Key Lab Microwave Imaging Technol Beijing Peoples R China

An unsupervised classification algorithm utilising both polarimetric scattering mechanisms (PSMs) of hybrid-polarity data and the Wishart classifier is proposed. The initial scattering categories of the proposed algorithm are derived from the roll-invariant m-chi classification algorithm. Pixels with no clearly defined dominant PSM are excluded, and the resulting categories are expanded into a specified number of classes. These derived classes are taken as training samples of the Wishart classifier. The effectiveness of the proposed algorithm is validated with the dataset over San Francisco.

关键词： synthetic aperture radar PSM San Francisco Wishart classifier hybrid polarimetric SAR data hybrid polarity data initial scattering categories polarimetric scattering mechanisms roll-invariant m-χ classification algorithm unsupervised classification algorithm

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Comparison of classification and supervised learning algorithms in assessing the hydraulic conditions of sewer collection systems: A case study of local sewer networks in Jinju City, Korea

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DESALINATION AND WATER TREATMENT 2018年 124卷 202-210页

作者： Ki, Seo Jin Lee, Chun-Sik Jung, Won-Hee Park, Hyun-Geoun Gyeongnam Natl Univ Sci & Technol Dept Environm Engn 33 Dongjin Ro Jinju Si 52725 Gyeongsangnam D South Korea

Accurate screening of sewer conditions from monitoring data contributes to maintaining their operations (in terms of water quality and quantity) safe as well as reducing their associated costs (for operation and maintenance). This study was designed to assess the performance deterioration in sewer systems using a series of data classification tools, namely classical classification and novel supervised learning algorithms. The hydraulic data available for four sewer systems at Jinju City in Korea in a daily format during the monitoring period of 2013-2017 were provided as example data sets to those algorithms, which were evaluated independently with 70% training and 30% test data sets randomly divided. A self-organizing map (SOM) with a specialty in extracting hidden patterns in data was used to classify the data sets into three warning levels in the absence of any definite warning criteria for individual parameters. Our findings showed that three supervised learning algorithms achieved comparable performance in predicting warning levels defined from SOM to exiting classification algorithm in terms of accuracy and error rate. The network architecture optimized for supervised learning algorithms, in fact, varied significantly depending on the data sets, including that with additional variables on top of the original data set. In contrast, exiting classification algorithm unexpectedly produced high error rates in case that the hydraulic parameters had low coefficient of variation values reaching as high as 16%. Overall, these results demonstrated that novel supervised learning algorithms were more universally applicable for the assessment of hydraulic and/or water quality conditions in sewer systems than classical classification algorithm, regardless of the amount of variability in the data sets.

关键词： Supervised learning algorithms classification algorithm Self-organizing map Hydraulic parameters Water quality Sewer systems

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Financial customer classification by combined model

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第2期8卷 431-446页

作者： Lin, Cong Zheng, Jinju Ningbo Univ Technol Ningbo 315000 Zhejiang Peoples R China

This paper explores the pros and cons of different algorithm models on the same selection problem, and then uses the combined prediction theory to obtain a new combined prediction model to explore its prediction accuracy. The actual problem to be solved is to help financial institutions to scientifically classify customers who choose financial products. We select the bank data set in the UCI database, which is derived from the survey data of a customer conducted by a financial institution in Portugal for a wealth management product. Decision tree C5.0 algorithm, naive Bayes classification algorithm and binary logit model are individually used to carry out a single model of empirical research on financial product customer classification. Through the empirical analysis of the five combination models, it is concluded that in the model that uses the least squares weighting method to determine the weight, the weight appears negative, which does not conform to the actual situation. The model that is based on the least squares weighting method and the model that is based on the simple weighting method are excluded. In contrast, the arithmetic mean weighted model is better than the reciprocal variance weighted model and the reciprocal mean square model. The accuracy reaches 89.91%, which is 0.43% higher than the accuracy of a single model. It can be concluded that the model that is based on the arithmetic average weighting is a better combination forecasting model.

关键词： combined forecasting classification algorithm financial products

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