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Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

Science China(Information Sciences) 2014年第11期57卷 252-259页

作者： ZHOU KaiLe FU Chao YANG ShanLin School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education

Fuzzy c-means(FCM) algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index(CVI) is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3].

关键词： clustering fuzziness parameter fuzzy c-means(FCM) cluster validation cluster validity index

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Empirical analysis of the factors of government administrative preparation of China

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Journal of Computational and Theoretical Nanoscience 2015年第11期12卷 4352-4357页

作者： Long, Zhu Jiang, Dai Changyong, Liang Management School of Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China

A previous study conducted in Anhui Province by collecting data from 56 county-level governments in 2009 has shown that the factors of demographic, financial and geographical area have linear relationship with government administrative preparation. The administrative preparation of county-level government increased with the size of population, expenditure and geographical area. Geographical area has great impact on administrative preparation, followed by least impact of financial and population. Taking a county government for example, the geographical area is unchanged but household population changed slowly. So the main factor affected the total administrative preparation is financial expenditure. In the process of approving the county government administrative preparation should consider several factors, such as population movements around. The main objective of this study is to prove that administrative preparation should be based on resident population rather than household population. © 2015 American Scientific Publishers.

关键词： Finance

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Software project management based on dynamic programming algorithm

Software project management based on dynamic programming alg...

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International Conference on Information Sciences,Machinery,Materials and Energy（ICISMME 2015）

作者： Zhidong Li Xiaohuan Yu School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Nanjing University of Posts and Telecommunications

With the rapid development of computer technology,software engineering disciplines has developed rapidly not only in the aspect of theory,but is increasingly important in the practical application,and gradually formed methodologies,tools and management of three *** these three factors,the study of software project management is relatively backward,and even become a major obstacle in the development of software engineering ***,in view of software engineering discipline facing the problem,this paper proposed dynamic programming algorithm should be applied to software engineering management.

关键词： computer technology software engineering management dynamic programming

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Approximation algorithms for two-stage supply chain scheduling of production and distribution

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International Journal of Systems Science: Operations and Logistics 2015年第2期2卷 78-89页

作者： Cheng, Bayi Li, Kai Hu, Xiaoxuan School of Management Hefei University of Technology Hefei China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China

In this paper, we investigate a class of two-stage optimisation problems for manufacturers in supply chains. The objective is to optimise production and outbound distribution. In the production part, the manufacturer has identical batch-processing machines with limited capacity in size. The processing times and sizes of jobs are determined by the customer. As long as the total size of jobs in a batch does not exceed the machine capacity, the batch can be processed on any machine. The processing of a batch cannot be interrupted until all the jobs in the batch have been completed. In the distribution part, the manufacturer assigns the completed jobs in groups and delivers them to the customer with its own vehicles. We propose the model of minimising service span, which lasts from the processing of the first job to the delivery of the last job. We investigate two types of machine configuration, including single machine and parallel machines. In the vehicle configuration, we consider two cases including infinite vehicles and a fixed number of vehicles. We show all the problems under investigation are NP-hard. For each problem, we design an effective polynomial time algorithm. We analyse the time complexity and show the performance guarantees of the algorithms. © 2014, © 2014 Taylor & Francis.

关键词： approximation algorithm arbitrary-size jobs batch-processing machines supply chain scheduling

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

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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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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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