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CO-RS: A new method for opinion mining based on semi-supervised learning and ensemble learning

ICIC Express Letters 2015年第7期9卷 1803-1808页

作者： Wang, Gang Li, Ningning School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei University of Technology No. 193 Tunxi Road Hefei China

In recent years many machine learning methods have been proposed for opinion mining and the effectiveness of applying them to opinion mining has also been approved theoretically. However, machine learning methods encounter many problems when they are used in practice. One of the problems is to construct learning systems with strong generalization ability. In this study, we propose, Co-RS, a semi-supervised method for opinion mining based on co-training and Random Subspace. Co-RS tries to get the strong generalization ability by integrating the strength of two machine learning paradigms, i.e., semi-supervised learning and ensemble learning. Moreover, four public opinion datasets were investigated to verify the effectiveness of proposed method. Empirical results reveal that Co-RS gets the best result among the compared methods. These results illustrate that Co-RS can be used as a viable method for opinion mining. © 2015, ISSN 1881-803X.

关键词： Sentiment analysis

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Collaborative optimization of profit and cost of hybrid cloud storage service

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International Journal of u- and e- Service, Science and Technology 2015年第2期8卷 185-196页

作者： Sun, Chao-Ping Zhao, Jing Ruan, Su-Mei School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China School of Business Anhui University of Finance and Economics Bengbu China

Aiming at hybrid cloud storage service (HCSS), this paper conducts research on collaborative optimization of corresponding profits and costs. Profit function of HCSS considers flexibility, extendibility, decrease in investment, improvement on access speed, data sharing, disaster recovery, and green IT, while cost function of HCSS considers data transmission, data storage, and facilities of hardware and software. Based on profit and cost functions of HCSS, a goal programming model is constructed to collaboratively optimize profits and costs of HCSS. The model is validated by making a comparative analysis in two representative contexts. © 2015 SERSC.

关键词： Profitability

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A study on DS-SVM-based forecast model from the view of information fusion

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International Journal of u- and e- Service, Science and Technology 2015年第2期8卷 395-404页

作者： Zhu, Weidong Sun, Yibo Wu, Yong Liu, Jingyu School of Economics Hefei University of Technology HefeiAnhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education HefeiAnhui China Institute of Industrial Information Hefei University of Technology HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education HefeiAnhui China School of Management Hefei University of Technology HefeiAnhui China

Dempster-Shafer Theory is specially advantaged in information fusion, while Support Vector Machine (SVM) can well deal with high-dimensional limited sample data. This Article firstly forecasts the data samples by categories with multiple SVMs, and hence based thereon, fuses the resulting information from multiple SVM models by using DS theory. At the end, Anderson's Iris data set is used to simulate the system of the created DS-SVM model, which shows that the approaches proposed in this Article can not only increase the accuracy rate of the SVM forecasting model, but also improve the propensity scores of the SVM forecasting model. © 2015 SERSC.

关键词： Support vector machines

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Electricity load forecasting in smart grid based on residual GM (1, 1) model

Electricity load forecasting in smart grid based on residual...

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2013 International Conference on Mechatronics and Automatic Control Systems, ICMS 2013

作者： Shen, Jianxin Wang, Haijiang Yang, Shanlin Key Lab of Process Optimization and Intelligent Decision-making Hefei University of Technology Hefei China

ISBN: (纸本)9783319012728

The construction of smart grid has put forward higher requirements on deployment accuracy of the energy. Power generation and electricity sectors have carried out more accurate data analysis and forecasting. In this context, a residual GM (1, 1) model is proposed. This model can overcome the lack of traditional grey model and make accurate forecasting of electricity consumption in smart grid. Finally, numerical examples demonstrate that this method can efficiently improve the prediction accuracy. © Springer International Publishing Switzerland 2014.

关键词： Smart power grids

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Electricity consumption prediction based on non-stationary time series GM (1, 1) model and its application in power engineering

Electricity consumption prediction based on non-stationary t...

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2013 International Conference on Mechatronics and Automatic Control Systems, ICMS 2013

作者： Wang, Xiaojia China Key Lab of Process Optimization and Intelligent Decision-making Hefei University of Technology Hefei China

ISBN: (纸本)9783319012728

The construction of smart grid will comprehensively enhance the intelligent level of every step in the power grid of our country. The data prediction ability determines the quality of smart grid. This paper addresses situations in which the prediction accuracy of the Grey Model (GM (1, 1) model) is high for non-negative smooth monotonic sequences but inadequately low for non-stationary sequences, and isolates the trending sequence from the non-stationary time series using a numerical filtering algorithm, which is then used to make predictions. Numerical examples demonstrate that this method can improve the prediction accuracy of the GM (1, 1) model. © Springer International Publishing Switzerland 2014.

关键词： Forecasting

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Based IGARCH error correction of the PLS-SVR short-term load forecasting

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2012 International Conference on Electrical and Electronics Engineering, ICEE 2012

作者： Chen, Zhiqiang Yang, Shanlin Hou, Liqiang Key Lab of Process Optimization and Intelligent Decision-Making Hefei University of Technology Hefei Anhui 230009 China

ISBN: (纸本)9781461449805

Due to the complexity in the influencing factors of the prediction accuracy, using single forecasting method to improve the prediction accuracy is just impossible in practice. In this chapter, the partial least square (PLS)method was used to diminish the sample input data, which can improve the traditional Support Vector Regression (SVR) for short-time electricity load. Then, there is error sequence between the predictive value and the actual value, and the error sequence was considered as the forecasting data, which has the characteristics of obvious peak and fat tail. Next, Integrated Generalized Autoregressive Conditional Heteroskedasticity (IGARCH) model was used to build the electricity load error predicted model, and modify the original predictive value. Lastly, the forecasting method of this chapter based on PJM historical data was verified. The result shows that the mean absolute percentage error (MAPE) and mean square prediction error (MSPE) are 3.56 % and 1.75 %, respectively. Compared to other traditional predictive value, the model presented in this chapter has higher accuracy, which can be applied to predict the short-term electricity load. © 2014 Springer Science+Business Media New York.

关键词： Error correction

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COS-training: A new semi-supervised learning method for keyphrase extraction based on co-training and SMOTE

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ICIC Express Letters, Part B: Applications 2015年第1期6卷 233-238页

作者： Wang, Gang Yang, Shanlin Ma, Jian School of Management Hefei University of Technology No. 193 Tunxi Road Hefei China Ministry of Education Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of Technology No. 193 Tunxi Road Hefei China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

As keyphrase is a small set of words that can best represent a document, they play significant roles in varieties of text-related tasks. In recent years, many unsupervised and supervised methods have been proposed for keyphrase extraction. However, keyphrase extraction is an imbalanced classification problem in nature and contains many unlabeled data, which have not been paid attention to in the previous studies. In this research, a new semi-supervised learning method, COS-training, is proposed for keyphrase extraction based on co-training and SMOTE. For the testing and illustration purpose, a keyphrase extraction dataset is selected to verify the effectiveness of the proposed method. Empirical results reveal that COS-training is a potential solution for keyphrase extraction. Among the compared methods, COS-training gets the best result. Al l these results illustrate that COS-training can be used as an alternative method for keyphrase extraction.

关键词： Extraction

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The green project of data management for industrial integration

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2015年第6期8卷 333-342页

作者： Tian, Yiming Zhu, keyu Pan, Ruoyu Liang, Changyong School of Computer and Information Hefei University of Technology Anhui Transport Interconnection Management Center Hefei China Institute of Computer Network Systems Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China Key Technologies and Equipment of IOT of Highway Traffic Anhui Engineering Technology Research Center Hefei China

The green project fulfills the requests of industrial management and application by sharing data rather than isolating. The data center with energy-efficient equipment is correspondingly effective in industrial service, management and construction of information. This study suggests a green data center in the two-level framework as a basic factor in industrial integration with potent procedures. In the case of transportation, it processed validating data to resolve logistics and emergency operations. © 2015 SERSC.

关键词： Information management

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Mid-term load forecasting based on modified grey model

Mid-term load forecasting based on modified grey model

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2013 International Conference on Mechatronics and Automatic Control Systems, ICMS 2013

作者： Wang, Haijiang Yang, Shanlin Key Lab of Process Optimization and Intelligent Decision-making Hefei University of Technology Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9783319012728

The construction of smart grid put forward higher requirements on deployment accuracy of the energy. Power generation and electricity sectors have carried out more accurate data analysis and forecasting. In this context, we provide a Gauss-Chebyshev GM(1,1) model, This model could overcome the lack of traditional grey model and made accurate forecasting of electricity consumption in smart grid, Finally, numerical examples demonstrate that this method can efficiently improve the prediction accuracy. © Springer International Publishing Switzerland 2014.

关键词： Smart power grids

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Electricity consumption forecasting based on a class of new GM (1, 1) model

Electricity consumption forecasting based on a class of new ...

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2013 International Conference on Mechatronics and Automatic Control Systems, ICMS 2013

作者： Yao, Mei Wang, Xiaojia School of Mathematics Hefei University of Technology Hefei China Key Lab of Process Optimization and Intelligent Decision-making Hefei University of Technology Hefei China

ISBN: (纸本)9783319012728

In recent years, with the development of society and the progress of science and technology, the GM (1, 1) model has been widely applied in economy, management, industry, control, social development plan, etc. But the GM (1, 1) model in the application process has encountered a low accuracy of predictions. Therefore, the improvement and optimization research of GM (1, 1) model is an important issue. In order to improve the prediction accuracy of GM (1, 1) model, it is necessary to consider reconstructing the background value of the model, which can improve the quality of the forecasting model. This paper discusses the improved GM (1, 1) model. The improved model has better prediction accuracy. It is used to forecast the society electricity consumption in eastern china, and the specific numerical examples demonstrate that this method can improve the simulation and the prediction accuracy. © Springer International Publishing Switzerland 2014.

关键词： Forecasting

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