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检索条件"主题词=Classification algorithm"

共 544 条记录，以下是521-530 订阅

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Flexible Job Shop Scheduling Rules Mining Based on Random Forest

Flexible Job Shop Scheduling Rules Mining Based on Random Fo...

引用

作者： Yizhong Wang School of Aerospace Transport and Manufacturing Cranfield University

With the development of the global economy and customization,the manufacturing scheduling problem is increasingly *** job shops(FJSs) have to be more flexible and dynamic to handle these complex and various manufacturing *** at the dynamic scheduling problem of FJS,a method of mining scheduling rules from scheduling related historical data with industrial big data characteristics is *** the mining of scheduling rules,an improved random forest algorithm is proposed,which is suitable for mining scheduling rules from historical data related to large-scale,high-dimensional,and noisy *** results show that the scheduling rules obtained by the mining method have good performance in terms of scheduling performance and computational efficiency.

关键词： Flexible Job Shop scheduling rules mining random forest classification algorithm

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An Extraction Method of Hyponymy based on Multiple Data Sources Fusion

An Extraction Method of Hyponymy based on Multiple Data Sour...

引用

2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Shuqi Wang Xiao Feng Shuwu Zhang Chengcheng Yin Dearpartment of Signal and Information Systems Communication University of China Institute of Automation Chinese Academy of Sciences

Hyponymy is one of the most critical semantic relations,which contributes magnificently to semantic dictionary,information retrieval *** this paper,a method of extracting hyponymy is proposed based on multiple data sources fusion,which convert the extraction of hyponymy to the extraction of hypernyms for target ***,mining candidate hypernyms for the target words based on search engine,encyclopedia resources and core suffix ***,fusing the candidates from the above data *** last,the classification algorithm is used to filter the noise and extract the hypernyms,which is a quite mature machine learning *** is hyponymy between the target words and their correctly extracted *** experimental results show that the highest accuracy rate of hyponymy extraction reaches 0.832 using the proposed method.

关键词： hyponymy multiple data dources fusion mining candidate hypernyms classification algorithm hypernym extraction

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A Hybrid Monitoring Method for Robust Multivariate Process Control

A Hybrid Monitoring Method for Robust Multivariate Process C...

引用

SCIEI 2015 Singapore Conference

作者： Sung Ho Park Seoung Bum Kim Korea University/ Department of Industrial Management Engineering

Control chart,the most representative statistical process control technique,can be considered to share a common goal with classification algorithms in that both of them aim at estimating the decision boundaries that classify the observations into either in-control or the out-of-control *** main limitation that conventional control chart cannot take advantage of using out-of-control information can be eased by integrating classification algorithms with existing monitoring *** the present study,we proposed a hybrid scheme that combines T2 control chart and binary class classification algorithms by eliciting the nature of class *** and real case studies demonstrated that the proposed method can be effectively used for monitoring multivariate processes when their distributions are nonnormally distributed or unknown.

关键词： Multivariate control chart classification algorithm Hybrid monitoring statistic

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Improved Convolutional Neural Network for Lung Cancer Detection

Improved Convolutional Neural Network for Lung Cancer Detect...

引用

作者： Firdaous Essaf Yujian Li Seybou Sakho Pius Kwao Gadosey School of Computer Science and Technology Beijing University of Technology School of Artificial Intelligence Guilin University of Electronic Technology

The examination of the lungs is an important part of the annual physical examination. There are hundreds or thousands of cases in the physical examination, and each case contains many lung cross-sectional CT images. These all require professional doctors to screen for cases with pulmonary nodules one by one, which is not only a heavy workload but also a possibility of incorrect screening. Aiming at the above problems, a Convolutional Neural Network(CNN) is introduced to screen out the CT images for pulmonary nodules, and a classification algorithm based on CNN is proposed. The experimental results in the LIDC database show that compared with the widely used lenet-5 network, traditional methods, and other deep learning models, the use of customized convolutional neural networks improves the classification accuracy. The AUC value is 0.821 6, which is also the highest among several classifiers. Compared with other methods, this method can more accurately identify CT images of the lungs and can provide a more objective reference for clinical diagnosis as it can be used for CAD systems.

关键词： Pulmonary nodule Convolutional Neural Network classification algorithm deep learning

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Application of Ant Colony Optimization-SVM in Fault Diagnosis for Rectifier Circuit

Application of Ant Colony Optimization-SVM in Fault Diagnosi...

引用

2010 2nd IEEE International Conference on Information and Financial Engineering(ICIFE2010)

作者： Xu Binghui Taizhou Vocational & Technical College Taizhou 318000,China

Failure of rectifier circuit has the characteristics of latency and complexity,which leads to the difficulty to fault diagnosis for rectifier circuit.A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the *** experimental object is provided and the six ACO-SVM classifiers are developed to identify the following seven states of the experimental *** testing results demonstrate that the ACO-SVM classifier has higher diagnostic accuracy than normal support vector machine and BP neural network.

关键词： rectifier circuit ant colony optimization classifier fault diagnosis classification algorithm

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Research on Wireless Network Consumer Satisfaction Prediction Method 21

Research on Wireless Network Consumer Satisfaction Predictio...

引用

Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Jian Xu Xi Wang Wireless Network Optimization Center China Mobile Communications GroupFujian Co.Ltd. China

ISBN: (纸本)9781450384087

With 5G network globalization, consumers have higher requirements for telecom operators' services. It is necessary to predict consumer satisfaction for analyzing consumer requirements. Based on the understanding of telecommunications services, the wireless network consumer satisfaction prediction is divided into three sub-predictive models: network quality, promotional activities, and tariff packages. At the same time, a hybrid sampling algorithm based on support vector machine (HS-SVM) which is used to classify the consumer satisfaction imbalance dataset is proposed to predict the consumer satisfaction of these three sub-predictive models, and the consumer's overall satisfaction is obtained by merging the results of the three sub-predictive models. The validity of the model is verified by wireless network consumer satisfaction dataset compared with the popular five separate classification algorithms and SMOTE combined with the five classification algorithms. The experimental results show that the F-value and G-mean of the proposed algorithm are improved. The proposed method has better classification performance and stronger robustness in the prediction of wireless network consumer satisfaction.

关键词： classification algorithm Support vector machine (SVM) Synthetic minority over- sampling technique (SMOTE) Wireless network consumer satisfaction imbalanced data

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Study of Procrastination in Higher Vocational Education Based on Online Learning Data 22

Study of Procrastination in Higher Vocational Education Base...

引用

Proceedings of the 6th International Conference on Digital Technology in Education

作者： Fang Feng Mengxin Tang Wen Lei School of Information Technology in Education South China Normal University China

ISBN: (纸本)9781450398091

Academic procrastination is a common phenomenon in China's higher vocational education. Due to the weakening of the role of teacher supervisors and the lack of students' self-control, the academic procrastination of students in online learning is more likely to occur. At present, it has become a trend to use educational data mining and artificial intelligence technology to evaluate, predict and intervene in online learning, so as to solve the problem of practical teaching lag and improve the teaching effect of vocational education. In this paper, the data of "Computer Application Foundation" course of higher vocational students on Chaoxing platform is used to process the data by using K-means and DBSCAN clustering algorithms, and the performance of the two algorithms is evaluated by using the contour coefficient. The results show that the K-means algorithm has better performance. The students were divided into active learners, mild procrastinators and severe procrastinators by K-means clustering algorithm. Then, combined with decision tree (DT), neural network (NN) and Naive Bayes (NB) algorithm to verify the accuracy of K-means clustering algorithm in identifying the classification of students' procrastination tendency, this paper hopes to provide some advises for online learning procrastinators and encourage students to keep learning initiative and enthusiasm.

关键词： Academic procrastination classification algorithm Clustering algorithm Educational data mining

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Applied Biological Data Mining Based on Improved K-means Clustering algorithm And KNN Classifier in Protein Sub-cellular Localization

Applied Biological Data Mining Based on Improved K-means Clu...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Zhenfeng Lei Shunfang Wang School of Information Science and Engineering Yunnan University

With the completion of the human genome sequencing,a large number of data especially amino acid sequences floods into biological *** to analyze these data quickly and even predict the structure and function of protein correctly have become hot topics in recent *** this paper,we mainly study K-means clustering algorithm and KNN classifier in amino acid sequences of complicated data,which are applied in the prediction of protein sub-cellular *** many cases,fuzzy boundary and unbalance are frequently appeared among biological *** accuracy will be lower,if we make a prediction through traditional KNN and K-means clustering algorithm ***,in order to make clear the unbalance,we propose the within-class thought to make sure that training samples in each class around the testing sample are selected and we introduce membership to tell which class the testing sample belongs ***,we bring in rough sets and membership to solve the fuzzy ***,we apply correlation coefficient in the rough sets to better reflect the relationship among data *** experimental results based on protein sub-cellular localization prediction show that the methods proposed newly better work than the traditional methods.

关键词： data mining clustering algorithm classification algorithm protein sub-cellular localization

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Prediction Model based on Internet News Buzzword Data

Prediction Model based on Internet News Buzzword Data

引用

3rd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2019)

作者： Xuan Lei School of Control and computer Engineering North China Electric Power University

The partial classification algorithm is mainly used to predict the popularity of network news and to explore the best model to predict the popularity of network news, so as to help network news service providers predict the popularity of news before publication. The popularity of network news is predicted according to the data analysis process: first, UCI data sets are pre-processed;secondly, feature selection is conducted for the data sets by using recursive feature elimination algorithm;then modelling and analysis is carried out, and finally through the confusion matrix, risk map and ROC(Receiver Operating Characteristic) chart performance evaluation, the performance of the model is compared and analyzed. Through comparison, it is found that random forest is the best prediction model.

关键词： network news feature selection classification algorithm model evaluation

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Embedding Na?ve Bayes algorithm Data Model in Predicting Student Graduation

Embedding Na?ve Bayes Algorithm Data Model in Predicting Stu...

引用

作者： Ace C.Lagman Joseph Q.Calleja Ma.Corazon G.Fernando Joseph G.Gonzales John Benedict Legaspi John Heland Jasper C.Ortega Ronel F.Ramos Maria Vicky S.Solomo Regina C.Santos FEU Institute of Technology

In the Philippines,according to Philippine Authority of Statistics,there is an imbalance between the student enrollment and student *** half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on *** study aims to utilize how Na?ve Bayes algorithm-a data classification algorithm that is based on probabilistic analysis-can be used in educational data mining specifically in student *** study is focused on the application of the Na?ve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time,so proper remediation and retention policies can be formulated and implemented by institutions.

关键词： Na?ve Bayes machine learning algorithm data mining student graduation prediction Big Data Analytics classification algorithm

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