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Important sampling based active learning for imbalance classification

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Science China(Information Sciences) 2020年第8期63卷 196-209页

作者： Xinyue WANG Bo LIU Siyu CAO Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University College of Information Science and Technology Hebei Agricultural University

Imbalance in data distribution hinders the learning performance of classifiers. To solve this problem, a popular type of methods is based on sampling(including oversampling for minority class and undersampling for majority class) so that the imbalanced data becomes relatively balanced data. However,they usually focus on one sampling technique, oversampling or undersampling. Such strategy makes the existing methods suffer from the large imbalance ratio(the majority instances size over the minority instances size). In this paper, an active learning framework is proposed to deal with imbalanced data by alternative performing important sampling(ALIS), which consists of selecting important majority-class instances and generating informative minority-class instances. In ALIS, two important sampling strategies affect each other so that the selected majority-class instances provide much clearer information in the next oversampling process, meanwhile the generated minority-class instances provide much more sufficient information for the next undersampling procedure. Extensive experiments have been conducted on real world datasets with a large range of imbalance ratio to verify ALIS. The experimental results demonstrate the superiority of ALIS in terms of several well-known evaluation metrics by comparing with the state-of-the-art methods.

关键词： imbalance classification important sampling active learning oversampling undersampling

Multi-Class Sentiment Analysis of Social Media Data with Machine Learning Algorithms

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Computers, Materials & Continua 2021年第10期69卷 913-930页

作者： Galimkair Mutanov Vladislav Karyukin Zhanl Mamykova Al-Farabi Kazakh National University Almaty050040Kazakhstan

The volume of social media data on the Internet is constantly *** has created a substantial research field for data *** diversity of articles,posts,and comments on news websites and social networks astonishes ***,most researchers focus on posts on Twitter that have a specific format and length *** majority of them are written in the English *** relatively few works have paid attention to sentiment analysis in the Russian and Kazakh languages,this article thoroughly analyzes news posts in the Kazakhstan media *** amassed datasets include texts labeled according to three sentiment classes:positive,negative,and *** datasets are highly imbalanced,with a significant predominance of the positive *** resampling techniques(undersampling,oversampling,and synthetic minority oversampling(SMOTE))are used to resample the datasets to deal with this ***,the texts are vectorized with the TF-IDF metric and classified with seven machine learning(ML)algorithms:naïve Bayes,support vector machine,logistic regression,k-nearest neighbors,decision tree,random forest,and *** results reveal that oversampling and SMOTE with logistic regression,decision tree,and random forest achieve the best classification *** models are effectively employed in the developed social analytics platform.

关键词： Social media sentiment analysis imbalanced classes machine learning oversampling undersampling SMOTE russian Kazakh

How much information is lost when sampling driving behavior data?Indicators to quantify the extent of information loss

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Journal of Intelligent and Connected Vehicles 2020年第1期3卷 17-29页

作者： Jun Liu Asad Khattak Lee Han Quan Yuan Department of Civil Construction and Environmental EngineeringThe University of AlabamaTuscaloosaAlabamaUSA Department of Civil and Environmental Engineering The University of TennesseeKnoxvilleTennesseeUSA State Key Laboratory of Automotive Safety and Energy School of Vehicle and MobilityTsinghua UniversityBeijingChina

Purpose–Individuals’driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic *** incoming data can be sampled at rates ranging from one Hertz(or even lower)to hundreds of *** to capture substantial changes in vehicle movements over time by“undersampling”can cause loss of information and misinterpretations of the data,but“oversampling”can waste storage and processing *** purpose of this study is to empirically explore how micro-driving decisions to maintain speed,accelerate or decelerate,can be best captured,without substantial loss of ***/methodology/approach–This study creates a set of indicators to quantify the magnitude of information loss(MIL).Each indicator is calculated as a percentage to index the extent of information loss(EIL)in different *** overall information loss index named EIL is created to combine the MIL *** from a driving simulator study collected at 20 Hertz are analyzed(N=718,481 data points from 35,924 s of driving tests).The study quantiﬁes the relationship between information loss indicators and sampling ***–The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz,but the relationship is not *** four indicators of MILs,the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior *** sampling rates are higher than 2 Hz,all MILs are under 5 per cent for importation ***/value–This study contributes by developing a framework for quantifying the relationship between sampling rates,and information loss and depending on the objective of their study,researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.

关键词： Driver behaviours and assistance Sensor data processing Information loss Instantaneous driving decisions Sampling rate undersampling

Scattering Center Modeling Using Adaptive Segmental Compressive Sampling

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Journal of Beijing Institute of Technology 2017年第4期26卷 484-493页

作者： Qifeng Li Kunyi Guo Jiaxin Wang Xinqing Sheng Tianshu Liu Center for Electromagnetic Simulation School of Information and ElectronicsBeijing Institute of Technology Science and Technology on Special System Simulation Laboratory Beijing Simulation Center

In order to deal with aliasing distortions of Doppler frequencies shown in time-frequency representation（ TFR） with aspect undersampling,an approach using adaptive segmental compressive sampling according to the aspect dependencies of the scattering centers is proposed. The random noise problem induced by compressive sampling is solved by employing a series of signal processing techniques of filtering,image transformation and Hough Transform. Three examples are presented to verify the effectiveness of this approach. The comparisons between the built models and the precise scattered fields computed by a well-validated full-wave numerical method are investigated,and the results showgood agreements between each other.

关键词： scattering center undersampling compressive sensing time-frequency representation parameter estimation

Radial Basis Function Neural Network Based Super- Resolution Restoration for an Undersampled Image

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Journal of Beijing Institute of Technology 2004年第2期13卷 135-138页

作者：苏秉华金伟其牛丽红 School of Information Science and Technology Beijing Institute of Technology Beijing100081 China Institute of Optoelectronics Shenzhen University Shenzhen Guandong518060 China

To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.

关键词： super-resolution image restoration image processing neural networks undersampling

Credit Card Fraud Detection Using Weighted Support Vector Machine

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Applied Mathematics 2020年第12期11卷 1275-1291页

作者： Dongfang Zhang Basu Bhandari Dennis Black Model Development Department Comerica Bank Dallas USA

Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.

关键词： Support Vector Machine Binary Classification Imbalanced Data undersampling Credit Card Fraud

THE SOLUTION TO AN AMBIGUITY PROBLEM WITH NOISE PROTECTION IN NUMBER SYSTEM

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Journal of Electronics(China) 2000年第2期17卷 123-131页

作者： Lin Yunsong Huang Yong Xiao Xianci (College of Electronic Engineering, UEST of China, Chengdu 610054)

The relationship between the discrete Fourier transform (DFT) and the symmetrical/asymmetrical number system (SNS/ANS) is introduced in this paper. And the influence of noise upon the solution to the ambiguity problem in number system is also discussed. The principle of noise insensitive solution to the ambiguity in ANS system is extended to SNS system. The unambiguous bandwidth equations with noise protection in SNS are presented, based on which a real time noise insensitive algorithm in SNS for resolving undersampling ambiguous frequency is proposed.

关键词： Unambiguous bandwidth Residue error undersampling