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Crowdsourcing label quality: a theoretical analysis

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Science China(Information Sciences) 2015年第11期58卷 113-124页

作者： WANG Wei ZHOU Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University

Crowdsourcing has been an effective and efficient paradigm for providing labels for large-scale unlabeled data. In the past few years, many methods have been developed for inferring labels from the crowd,but few theoretical analyses have been presented to support this popular human-machine interaction *** this paper, we theoretically study the quality of labels inferred from crowd workers by majority voting and provide an analysis of label quality that shows that the label error rate decreases exponentially with the number of workers selected for each task. We also study the problem of eliminating low-quality workers from the crowd,and provide a conservative condition for eliminating low-quality workers without eliminating any non-low-quality worker with high probability. We also provide an aggressive condition for eliminating all low-quality workers with high probability.

关键词： unlabeled data crowdsourcing majority voting label quality worker selection

Pulmonary Diseases Decision Support System Using Deep Learning Approach

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Computers, Materials & Continua 2022年第10期73卷 311-326页

作者： Yazan Al-Issa Ali Mohammad Alqudah Hiam Alquran Ahmed Al Issa Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk UniversityIrbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid 22110Jordan Pediatrician Mediclinic HospitalAl Ain14444UAE

Pulmonary diseases are common throughout the world,especially in developing *** diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently *** general,pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert *** recent years,many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung *** this paper,the performance of four popular pretrained models(namely VGG16,DenseNet201,DarkNet19,and XceptionNet)in distinguishing between different pulmonary diseases was *** the best of our knowledge,this is the first published study to ever attempt to distinguish all four cases normal,pneumonia,COVID-19 and lung opacity from ChestX-Ray(CXR)*** models were trained using Chest-X-Ray(CXR)images,and statistically tested using 5-fold cross *** individual models,XceptionNet outperformed all other models with a 94.775%accuracy and Area Under the Curve(AUC)of Receiver Operating Characteristic(ROC)of 99.84%.On the other hand,DarkNet19 represents a good compromise between accuracy,fast convergence,resource utilization,and near real time detection(0.33 s).Using a collection of models,the 97.79%accuracy achieved by Ensemble Features was the highest among all surveyed methods,but it takes the longest time to predict an image(5.68 s).An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time,such a dependable system can be used in rural areas and various healthcare sectors.

关键词： Pulmonary diseases deep learning lung opacity classification majority voting ensemble features

Robust Interactive Method for Hand Gestures Recognition Using Machine Learning

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Computers, Materials & Continua 2022年第7期72卷 577-595页

作者： Amal Abdullah Mohammed Alteaimi Mohamed Tahar Ben Othman Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia BIND Research Group College of ComputerQassim UniversityBuraydah51452Saudi Arabia

The Hand Gestures Recognition(HGR)System can be employed to facilitate communication between humans and computers instead of using special input and output *** devices may complicate communication with computers especially for people with *** gestures can be defined as a natural human-to-human communication method,which also can be used in human-computer *** researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable *** aims to develop a powerful hand gesture recognition model with a 100%recognition *** proposed an ensemble classification model that combines the most powerful machine learning classifiers to obtain diversity and improve *** majority voting method was used to aggregate accuracies produced by each classifier and get the final classification *** model was trained using a self-constructed dataset containing 1600 images of ten different hand *** employing of canny’s edge detector and histogram of oriented gradient method was a great combination with the ensemble classifier and the recognition *** experimental results had shown the robustness of our proposed *** Regression and Support Vector Machine have achieved 100%*** developed model was validated using two public datasets,and the findings have proved that our model outperformed other compared studies.

关键词： Hand gesture recognition canny edge detector histogram of oriented gradient ensemble classifier majority voting

Improving Decision Tree Performance by Exception Handling

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International Journal of Automation and computing 2010年第3期7卷 372-380页

作者： Appavu Alias Balamurugan Subramanian S.Pramala B.Rajalakshmi Ramasamy Rajaram Department of Information Technology Thiagarajar College of Engineering Madurai India Department of Computer Science Thiagarajar College of Engineering Madurai India

This paper focuses on improving decision tree induction algorithms when a kind of tie appears during the rule generation procedure for specific training datasets. The tie occurs when there are equal proportions of the target class outcome in the leaf node＇s records that leads to a situation where majority voting cannot be applied. To solve the above mentioned exception, we propose to base the prediction of the result on the naive Bayes （NB） estimate, k-nearest neighbour （k-NN） and association rule mining （ARM）. The other features used for splitting the parent nodes are also taken into consideration.

关键词： Data mining classification decision tree majority voting naive Bayes (NB) k nearest neighbour (k NN) association rule mining (ARM)

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Mining and Integrating Reliable Decision Rules for Imbalanced Cancer Gene Expression Data Sets

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Tsinghua Science and Technology 2012年第6期17卷 666-673页

作者： Hualong Yu 1 , Jun Ni 2 , Yuanyuan Dan 3 , Sen Xu 4 1. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China 2. Department of Radiology, Carver College of Medicine, The University of Iowa, Iowa City, IA 52242, USA 3. School of Biology and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China 4. School of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China Department of Radiology Carver College of Medicine The University of Iowa Iowa City IA 52242 USA School of Biology and Chemical Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China School of Information Engineering Yancheng Institute of Technology Yancheng 224051 China

There have been many skewed cancer gene expression datasets in the post-genomic era. Extraction of differential expression genes or construction of decision rules using these skewed datasets by traditional algorithms will seriously underestimate the performance of the minority class, leading to inaccurate diagnosis in clinical trails. This paper presents a skewed gene selection algorithm that introduces a weighted metric into the gene selection procedure. The extracted genes are paired as decision rules to distinguish both classes, with these decision rules then integrated into an ensemble learning framework by majority voting to recognize test examples; thus avoiding tedious data normalization and classifier construction. The mining and integrating of a few reliable decision rules gave higher or at least comparable classification performance than many traditional class imbalance learning algorithms on four benchmark imbalanced cancer gene expression datasets.

关键词： cancer gene expression data class imbalance paired differential expression genes decision ruleensemble learning majority voting

维普期刊数据库

Predicting mobile users’behaviors and locations using dynamic Bayesian networks

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Journal of Management Analytics 2016年第3期3卷 191-205页

作者： Jianrong Hou Hui Zhao Xiaofeng Zhao Jie Zhang Antai College of Economics and Management Shanghai Jiao Tong UniversityShanghaiChina School of Computer Science and Software Engineering East China Normal UniversityShanghaiChina University of Mary Washington FredericksburgUSA

This paper studies the traveling location prediction problem for detecting whether mobile users will leave their living area and where they will *** investigate the hidden connections between users’behaviors in different locations and online social *** combine dynamic Bayesian networks with a majority voting model which is based on social interaction information to estimate the users’behaviors and predict the *** analyzing Instagram media records,spanning a period of 3 months,we explore rarely visited locations,which are often ignored as noise in previous *** comparison,our model,using Instagram data with two existing location prediction models,shows that(1)our location prediction is more accurate and robust in both the general location and the location outside the living area;(2)social relations are instrumental in the location prediction as social interaction information can increase the accuracy of the prediction.

关键词： location prediction dynamic Bayesian network majority voting social interaction Instagram

Research on Copyright Protection Method of Material Genome Engineering Data Based on Zero-Watermarking

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Journal on Big Data 2020年第2期2卷 53-62页

作者： Lulu Cui Yabin Xu Beijing Key Laboratory of Internet Culture and Digital Dissemination Research Beijing100101China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Information Science and Technology UniversityBeijing100101China School of Computer Beijing Information Science&Technology UniversityBeijing100101China

In order to effectively solve the problem of copyright protection of materials genome engineering data,this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking ***,the important attribute values are selected from the materials genome engineering database;then,use the method of remainder to group the selected attribute values and extract eigenvalues;then,the eigenvalues sequence is obtained by the majority election method;finally,XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication *** a copyright dispute requires copyright authentication for the database to be ***,the zero-watermarking construction algorithm is used to obtain an eigenvalues sequence;then,this sequence is XORed with the watermarking information stored in the third-party authentication center to obtain copyright information ***,the ownership is determined by calculating the similarity between copyright information to-be-detected and copyright information that has practical *** experimental result shows that the zero-watermarking method proposed in this paper can effectively resist various common attacks,and can well achieve the copyright protection of material genome engineering database.

关键词： Material genome engineering copyright protection zero-watermarking majority voting