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2018 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献

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Forecast Of Logistics Demand Based On Grey Deep Neural Network Model 基于灰色深度神经网络模型的物流需求预测
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527006
Wenjing Yuan, Jian-Hua Chen, Jingjing Cao, Zeyi Jin
{"title":"Forecast Of Logistics Demand Based On Grey Deep Neural Network Model","authors":"Wenjing Yuan, Jian-Hua Chen, Jingjing Cao, Zeyi Jin","doi":"10.1109/ICMLC.2018.8527006","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527006","url":null,"abstract":"Aiming at the current problem of limited feature extraction ability and prediction accuracy of shallow neural networks, this paper proposes a grey deep neural network model by combining grey model and stacked autoencoder (SAE) to take advantage of their complementary merits for the particularity of 10gistics demand forecasting issue. Multiple grey model processes the original data first, then the output data is input into stacked autoencoder prediction (SAEP) model, applying SAE for feature representation at the bottom and extreme learning machine (ELM) for accurate prediction at the top, then the model gets a final prediction value. The proposed method is applied in the empirical research on the logistics demand in a Brazilian logistics company, and the predictive value obtains a relatively better accuracy than ordinary grey neural network model, thus the effectiveness of the proposed model has been verified.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133109007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Multi-Label Cost-Sensitive Feature Selection Algorithm In Incomplete Data 不完整数据下的多标签代价敏感特征选择算法
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526938
Qin Huang, Wenbin Qian, W. Shu, Binglong Wu, Shuangshuang Feng
{"title":"Multi-Label Cost-Sensitive Feature Selection Algorithm In Incomplete Data","authors":"Qin Huang, Wenbin Qian, W. Shu, Binglong Wu, Shuangshuang Feng","doi":"10.1109/ICMLC.2018.8526938","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526938","url":null,"abstract":"Cost-sensitive feature selection is an important research issue in both machine learning and data mining. Most existing cost-sensitive feature selection work deal with the single-label data. However, in real applications, the data usually is multi-label, continuous and incomplete because of the technology or cost limitations during data collection. To alleviate this problem, a cost-sensitive feature selection algorithm is proposed here for incomplete neighborhood multi-label which can implement feature selection based on considering about the weighted test cost. The experimental results show that our algorithm can select a low-cost feature subset without losing the classification accuracy. The effectiveness and feasibility of the proposed algorithm is verified by the performance on the three Mulan datasets.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133510915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Research On Intelligent Evaluation Method For Machining State Oriented To Process Quality Control 面向加工质量控制的加工状态智能评价方法研究
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526989
Liping Zhao, Feng-Xian Yan, Lu Wang, Yiyong Yao
{"title":"Research On Intelligent Evaluation Method For Machining State Oriented To Process Quality Control","authors":"Liping Zhao, Feng-Xian Yan, Lu Wang, Yiyong Yao","doi":"10.1109/ICMLC.2018.8526989","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526989","url":null,"abstract":"The dynamic control of process quality is of great significance to improve the intellectualization of manufacturing process. The real-time monitoring and evaluation of machining state provides support for the intelligent control of process quality. In view of the timevarying, coupling and dynamic characteristics of monitoring parameters, as well as the real-time dynamic correlation and nonlinear relationship between the processing state and the product quality, this paper uses the Stacked Auto-encoder (SAE) to optimize the multidimensional real-time monitoring parameters in the machining process. By using the hybrid model of SAE-BP neural network, the nonlinear mapping relation between multidimensional monitoring parameters and the processing state is characterized adaptively and the dynamic intelligent evaluation of the machining state in the intelligent manufacturing process is realized. Taking an experimental platform as an example, the validity of the dynamic intelligent evaluation of the SAE-BP neural network used in processing state is verified. The proposed method provides support for the real-time dynamic evaluation of machining state.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"412 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116136049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A Decomposition-Based Partition Method For Matrix-Multiplication In The Mapreduce Model Mapreduce模型中基于分解的矩阵乘法划分方法
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526930
K. Jea, Chih-Wei Hsu, Yen-Lin Chen
{"title":"A Decomposition-Based Partition Method For Matrix-Multiplication In The Mapreduce Model","authors":"K. Jea, Chih-Wei Hsu, Yen-Lin Chen","doi":"10.1109/ICMLC.2018.8526930","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526930","url":null,"abstract":"Matrix multiplication is widely used in various applications of linear algebra. The efficiency of matrix multiplication in the MapReduce model is bounded by the workload of intermediate key-value pairs and transmission cost between the map phase and the reduce phase. An appropriate partition of matrix data can reduce unnecessary transmission cost of the two phases. A matrix decomposition based method is proposed in this paper to reduce the transmission cost and provides effective matrix multiplication computation. The experimental result shows that the proposed method can lower the transmission cost as the matrix size increases.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122033679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Electromechanical Control Based On Artificial Neural Network 基于人工神经网络的机电控制
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526973
Ming-Chao Yu, Gongfa Li, Ying Sun, Bo Tao, Guozhang Jiang, Shuang Xu
{"title":"Electromechanical Control Based On Artificial Neural Network","authors":"Ming-Chao Yu, Gongfa Li, Ying Sun, Bo Tao, Guozhang Jiang, Shuang Xu","doi":"10.1109/ICMLC.2018.8526973","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526973","url":null,"abstract":"According to the gradual rise of current theoretical research on artificial neural network, this article made a preliminary overview of the application of the artificial neural network control system, and then combined with the problems of intelligent electromechanical control field, to discuss the application method of artificial neural network in the control system of electromechanical integration. This paper first introduces the background of artificial neural network and electromechanical integration, and then summarizes the status quo of expert system, fuzzy logic and neural network applied in the electromechanical integration control system in the field of artificial intelligence. Then, this paper focuses on how to combine artificial neural network with electromechanical control system, and analyzes the advantages and disadvantages and practical application prospects of artificial neural network. Finally, some problems that may be further improved by artificial neural network in the field of electromechanical control are put forward, and a preliminary prospect for its future application value is made.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125472209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Feature Grouping Based On Ga And L-Gem For Human Activity Recognition 基于Ga和L-Gem的特征分组人类活动识别
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527017
Yi-Wen Xue, Jing Liu, Jiamin Chen, Yun-Tao Zhang, Renhua Cao
{"title":"Feature Grouping Based On Ga And L-Gem For Human Activity Recognition","authors":"Yi-Wen Xue, Jing Liu, Jiamin Chen, Yun-Tao Zhang, Renhua Cao","doi":"10.1109/ICMLC.2018.8527017","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527017","url":null,"abstract":"Human Activity Recognition is useful in many applications such as video surveillance and health care for elderly. Multiple Classifier System (MCS) has been proved to be better than single classifiers theoretically and empirically. Diversity is the key to improving the performance of an MCS. Feature grouping is one of the common methods. In this paper, we propose a new feature grouping method that uses Localized Generalization Error Model(L-GEM) as an evaluation criterion for optimizing MCS. Genetic Algorithm (GA) is used to evolve the weights of the members of MCS so that a set of diverse classifiers is combined to build an MCS which minimizes the localized generalization error. Experiments is performed based on a human activity recognition dataset and the results are compared with random subspace method to show that the MCS performs better.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129623084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Distance Based On Neighborhood Classifier and Attribute Reduction 基于距离的邻域分类器与属性约简
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527025
Yuan Gao, Keyu Liu, Jingjing Song, Xiangjian Chen, Xibei Yang
{"title":"Distance Based On Neighborhood Classifier and Attribute Reduction","authors":"Yuan Gao, Keyu Liu, Jingjing Song, Xiangjian Chen, Xibei Yang","doi":"10.1109/ICMLC.2018.8527025","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527025","url":null,"abstract":"In the neighborhood rough set model, with the increasing of the size of information granules, the neighborhood classifier based on the majority voting rule is easy to misjudge the classes of testing samples. To remedy this deficiency, a strategy of attribute reduction based on the idea of the minimum mean distance is proposed in this paper. Firstly, a neighborhood relation is presented by a distance function. Secondly, neighborhood mean distance classifier judges the class of testing sample with the minimum mean distance instead of the majority voting rule in the decision system. Finally, the experimental results on 8 UCI data sets tell us that the reduct obtained by our strategy can not only decrease the conditional entropy, but also provide better classification performances in larger scale information granules.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"10 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120930183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
The Prediction of Offender Identity Using Decision-Making Tree Algorithm 基于决策树算法的罪犯身份预测
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527045
Shao-Chong Shi, Peng Chen, Peng-Hui Yuan, Chao Hou, Hong-Xia Ming
{"title":"The Prediction of Offender Identity Using Decision-Making Tree Algorithm","authors":"Shao-Chong Shi, Peng Chen, Peng-Hui Yuan, Chao Hou, Hong-Xia Ming","doi":"10.1109/ICMLC.2018.8527045","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527045","url":null,"abstract":"Theft of electric bicycle cases is a new type of property crime since 2008 in China. It is difficult to arrest thieves, because it is very hard to find the information about the identity of suspects due to fewer clues on the spot. In this paper, we develop a criminals’ native places prediction model that can assist police offices in mining investigative intelligence in order to identify and catch the offenders based on historical broken cases data about criminals. The sample broken cases data we use has five important attributes, to be more specific, method, time, district, object and sites, which are believed by police to be related with thieves’ native places. Decision Tree algorithm is utilized to create a decision tree in the modeling and prediction of criminals’ native places that corresponds to the sample data. At each level of the tree, Information Gain Ratio is computed for each attribute in order to be chosen as a splitting attribute if it gives us the highest Information Gain Ratio. We also perform different algorithms such as BayesNet, Logistic and NaïveBayes to analyse their performance. Obtained results show that Decision Tree can be deployed with a high degree of accuracy in the prediction of criminals’ native places.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115870675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Bayesian Theorem In Rough Set Background And Its Application To Recovering Image 粗糙集背景中的贝叶斯定理及其在图像恢复中的应用
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527019
Tian-Yi Zhang
{"title":"Bayesian Theorem In Rough Set Background And Its Application To Recovering Image","authors":"Tian-Yi Zhang","doi":"10.1109/ICMLC.2018.8527019","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527019","url":null,"abstract":"Bayesian theorem is widely used in the fields of machine learning and computer vision. But the classic Bayesian theorem only focuses on the situations with a finite or countable partition of sample space, which sets a limitation on its applications. This paper first generalizes it by means of conditional mathematical expectation, and proceeds to propose a conjecture about it. Second, this paper introduces the concept of rough lower probability in approximation space; thereafter, the multiplication rule, the law of total probability and Bayesian theorem in classic probability are generalized into the rough background. Thirdly, the application of the generalized Bayesian theorem into the computer is suggested.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116020526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Suiciderelated Text Classification With Prism Algorithm 基于Prism算法的自杀相关文本分类
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527032
Fatima Chiroma, Han Liu, Ella Haig
{"title":"Suiciderelated Text Classification With Prism Algorithm","authors":"Fatima Chiroma, Han Liu, Ella Haig","doi":"10.1109/ICMLC.2018.8527032","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527032","url":null,"abstract":"Raw but valuable user data is continuously being generated on social media platforms. This data is, however, more valuable when they are mined using different approaches such as machine learning techniques. Additionally, this user-generated data can be used to potentially save lives especially of vulnerable social media users, as several studies carried out have shown the correlation between social media and suicide. In this study, we aim at contributing to the research relating to suicide communication on social media. We measured the performance of five machine learning algorithms: Prism, Decision Tree, Naï;ve Bayes, Random Forest and Support Vector Machine, in classifying suicide-related text from Twitter. The results of the study showed that the Prism algorithm has outperformed the other machine learning algorithms with an F-measure of 0.84 for the target classes (Suicide and Flippant). This result, to the best of our knowledge, is the highest performance that has been achieved in classifying social media suicide-related text.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131713746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
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