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Disagreement-based semi-supervised learning method for enterprise credit risk assessment

ICIC Express Letters 2015年第6期9卷 1671-1676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

The enterprise credit risk assessment problem has long been regarded as an important and widely studied issue in both academia and industry. However, unla-beled data problem is paid less attention to in the credit risk assessment community. To fill in this research gap, one of popular disagreement semi-supervised learning methods, co-training, is introduced to exploit the unlabeled data to improve the enterprise credit risk assessment performance. Co-training allows one to start with just a few labeled instances to produce an initial weak classifier and then use unlabeled data to improve the performance. Experiments based on two credit datasets reveal that co-training SVM&DT (Support Vector Machine & decision Tree) gets the better performance than the compared methods. Based on these results, we can conclude that co-training SVM&DT can be used as an alternative method for enterprise credit risk assessment. © 2015 ICIC International.

关键词： decision trees

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

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

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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Evaluation on regional innovation performance of China's patent data based on two-stage model

Evaluation on regional innovation performance of China's pat...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Su Yang Changyong Liang School of Management Anhui University of Architecture Hefei China School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

ISBN: (纸本)9781467384810

Being paid great attention to its operating performance, patent is the key element for a country or area to gain long-term advantage in various competitions. This paper, based on Chinese patent data, proposes a two-stage model with factor analysis and clustering analysis. It also proposes a patent innovation performance evaluation system in view of the input-output index of twenty patents, giving factor analysis to principal components, combining the various indexes to create new factors through calculating, extracting and aggregating, and then with this new factor score, this article evaluates the patent innovation performance systematically using the clustering analysis. The results show that, obviously, patent modification can promote innovation patent performance, especially the second modification. And at last, this article provides suggestion and development strategies to the improvement of patent system.

关键词： decision support systems Conferences Logistics Informatics

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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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