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Business Intelligence and Financial Engineering (BIFE), 2012 Fifth International Conference on

Date 18-21 Aug. 2012

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Displaying Results 1 - 25 of 151
  • [Back cover]

    Publication Year: 2012 , Page(s): C4
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  • [Title page i]

    Publication Year: 2012 , Page(s): i
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  • [Title page iii]

    Publication Year: 2012 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2012 , Page(s): iv
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  • Table of contents

    Publication Year: 2012 , Page(s): v - xv
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  • Preface

    Publication Year: 2012 , Page(s): xvi - xvii
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  • Organizing Committee

    Publication Year: 2012 , Page(s): xviii
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  • Program Committee

    Publication Year: 2012 , Page(s): xix - xxi
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  • Reviewers

    Publication Year: 2012 , Page(s): xxii
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  • A Fuzzy Group Forecasting Model Based on BPNN for Wind Power Output

    Publication Year: 2012 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (257 KB) |  | HTML iconHTML  

    Many forecasting models have been developed for forecasting wind farm electricity output. In most situations, performance of models is problem-dependent. Thus, it is difficult for forecasters to choose the right technique for each unique situation. In order to overcome this problem, this paper integrates multiple models into an aggregated model to obtain further performance improvement. Firstly, three groups of BPNN forecasting models are designed, i.e. univariate BPNN models, the hybrid model of ARIMA and BPNN and the multivariate model. Each group of the models can be regarded as an expert in forecasting, and then the fuzzy theory is used to combine all these forecasting results into the final answer. Results show that this group forecasting model performs well in terms of accuracy and consistency. View full abstract»

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  • Reliability Analysis of Multi-Stress Accelerated Life Test Based on BP Neural Network

    Publication Year: 2012 , Page(s): 6 - 9
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (200 KB) |  | HTML iconHTML  

    To construct multi-stress accelerated life model, the traditional method utilizes the observed value acquired in the process of accelerated life test to build likelihood equations of accelerated model, however, the excessive parameters of multi-stress accelerated model will lead to the difficulty in solving pluralism likelihood equations. Based on the predictability and convergence of genetic algorithm optimum BP neural network, the multi-stress accelerated life model of genetic neural network is built. Take the level and reliability of accelerated stress in the accelerated life test as neural network input, then draw the scatter diagram by software and use the nonlinear least square to fit raw data to obtain the regression equation. Consequently, generate large quantities of test data, which shall be input into the neural network and optimize the weight and threshold value by genetic algorithm. By this means, conquer the blindness in selecting the original weight and threshold value. Finally, input constant stress and set reliability into well-trained network, thus get the predicting curve of reliability. Simulation results show that the method above is efficient and practical. View full abstract»

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  • A Triple Artificial Neural Network Model Based on Case Based Reasoning for Credit Risk Assessment

    Publication Year: 2012 , Page(s): 10 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (154 KB) |  | HTML iconHTML  

    For most credit risk assessment models, decision attributes and history data are of great importance in terms of accuracy of prediction. Decision attributes can be classified into two types: numerical and categorical. As these two types have different characteristics, there will be interference if they are used simultaneously in the same model. By applying the case based reasoning (CBR) and artificial neural network (ANN), this study attempts to use numerical and categorical attributes separately in different phases application of the model. For example, if numerical attributes are used in CBR to select similar cases, categorical attributes will be used as inputs of an ANN based on the cases selected. Therefore, interference caused by the different types of attributes is avoided and the accuracy is improved. As only similar history data are selected and input in the ANN, accuracy is improved further. With the idea above, a triple ANN-CBR model is designed in this paper. This model synthesizes advantages of CBR and ANN. Practical examples show that the model established in this paper is feasible and effective. Compared with other models, it has a better precision performance. View full abstract»

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  • Improving Financial Returns Using Neural Networks and Adaptive Particle Swarm Optimization

    Publication Year: 2012 , Page(s): 15 - 19
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (188 KB) |  | HTML iconHTML  

    For financial investment, the problem that we often encounter is how to extract information hidden in the volatile and noise data and forecast it into future. This study proposes a novel three-stage neural-network-based nonlinear weighted ensemble model. In proposed model, three different types neural-network base models, i.e., Elman network, generalized regression neural network (GRNN) and wavelet neural network (WNN) are generated by three non-overlapping training sets, further, they are optimized by improved particle swarm optimization (IPSO) with adaptive nonlinear inertia weight and dynamic arccosine function acceleration parameters. Finally, a neural-network-based nonlinear weighted meta-model be produced by learning three neural-network base models through support vector machines (SVM) neural network. The superiority of the proposed approach is due to its flexibility to account for potentially complex nonlinear relationships that are not easily captured by single or linear models. Empirical results suggest that the novel ensemble model generally produces forecasts which, on the basis of out-of-sample forecast encompassing tests and comparisons of four different statistic measures, routinely dominate the forecasts from single modeling and linear modeling approach with two daily stock indices time series processes. View full abstract»

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  • An Ensemble of Fuzzy Sets and Least Squares Support Vector Machines Approach to Consumer Credit Risk Assessment

    Publication Year: 2012 , Page(s): 20 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (165 KB) |  | HTML iconHTML  

    Least squares support vector machines (LS-SVM), with excellent generalization performance and low computational cost, has been proven to be a useful tool in consumer credit risk assessment. It is a common assumption that the labels of the consumers are unchanged, which is contradictory with population drift. In this paper, we use a fuzzy membership of each input data to represent the impact of population drift on consumers' labels and the relative importance for the construction of the separating decision function, which is an ensemble of fuzzy sets and sparse LS-SVM. The purpose is to try to explain why an applicant should be rejected. Two UCI and an American credit card datasets are used to test the efficiency of our method and the result proves to be a satisfactory one. View full abstract»

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  • Personal Credit Assessment Based on KPCA and SVM

    Publication Year: 2012 , Page(s): 25 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (154 KB) |  | HTML iconHTML  

    Personal credit assessment is carried out by setting up a mathematical model to count, calculate and analyze the personal credit data. At present personal credit assessment has already became a kind of worldwide industry. In this paper we combine kernel principal component analysis and support vector machine to propose a new mathematical model based on KPCA and SVM. We extract personal credit data using KPCA, then use them to train SVM. Experiments show that the new method put forward in this paper is superior to other methods in assessing precision and assessing efficiency. View full abstract»

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  • Crude Oil Price Forecasting: A Transfer Learning Based Analog Complexing Model

    Publication Year: 2012 , Page(s): 29 - 33
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (159 KB) |  | HTML iconHTML  

    Most of the existing models for oil price forecasting only use the data in the forecasted time series itself. This study proposes a transfer learning based analog complexing model (TLAC). It first transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by analog complexing method. Finally, genetic algorithm is introduced to find the optimal matching between the two important parameters in TLAC. Two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price are used for empirical analysis, and the results show the effectiveness of the proposed model. View full abstract»

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  • A Three-stage Data Mining Model for Reject Inference

    Publication Year: 2012 , Page(s): 34 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (185 KB) |  | HTML iconHTML  

    Reject inference is a term that distinguishes attempts to correct models in view of the characteristics of rejected applicants. The main difficulty in establishing reject inference model is that the 'through-the-door' applicant population is unavailable. In this paper, we propose a hybrid data mining technique for reject inference. It is a three-stage approach: k-means cluster, support vector machines classification and computation of feature importance. By combining the samples of the accepted applicants and the new applicants, we obtain representative samples. To some extent, this is cost-free. Analytic results demonstrate that our method improves the predictive performance while still retaining interpretability. View full abstract»

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  • Case Study of Performance Management for PPP Projects

    Publication Year: 2012 , Page(s): 39 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (139 KB) |  | HTML iconHTML  

    As the widely usage of PPP model in the delivery of infrastructures all around the world, the performance of this procurement method is highly concerned. There have been many performance management techniques proposed in decades of practice, so this paper intended to study some of the successful cases and analyze their performance management practice, and also comparative study was conducted. Based on the framework of the European Foundation for Quality Management (EFQM) Excellence Model, this paper analyzed the Key Performance Indicators (KPIs) taken by the subject projects, and explained how could those measures can improve the performance of PPP projects. Some innovative measures are highly recommended by this paper after they have already been effectively applied and approved to be beneficial. View full abstract»

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  • A New Method of Computing Chinese Word Similarity Based on Statistics

    Publication Year: 2012 , Page(s): 43 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (248 KB)  

    Word semantic similarity is a very subjective concept and it is very difficult to get a similarity value close to human judgment. Chinese word semantic similarity research is relatively scarce due to its inherent complexity. This paper presents an approach to compute Chinese word semantic similarity based on statistical methods with word frequency contrast introduced (WFC-WS). Word semantic vectors are first obtained using co-occurrence and then extended with HIT-IR Tongyici Cilin (Extended). Word frequency contrast is introduced to filter the semantic vectors. Experiments show that the results of WFC-WS are closer to artificial standard compared with some similar methods. View full abstract»

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  • Regional Logistics Information Resources Integrate and Share

    Publication Year: 2012 , Page(s): 47 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (117 KB) |  | HTML iconHTML  

    Integration and Sharing of information are becoming increasingly important in logistics industry, but due to the limitations of logistics enterprises in integrated technology and capabilities of regional expansion, regional logistics information could not be shared and exchanged across enterprises. When we design the model of integration and sharing of regional logistics information resources, we need comprehensively understand the demands in the value chain, regional logistics companies should take into account the relevant impact in making their own strategies, so that the integration and sharing can realize win-win for all parties in the supply chain. This paper brings forward feasible model on the integration and exchange of regional logistics information resources, this model provides information flow, business flow and logistics services for regional logistics enterprises through innovative services and collaborative management. The existence of design model in sharing and exchange is sufficient to reduce logistics operating costs, improve capabilities of execution and responsiveness, and enhance their core competitiveness in fast, high-quality, low-cost logistics service. View full abstract»

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  • Envelopment-competition Pattern of E-Business Platform -- Insights from the Competition among Taobao, Baidu and Tencent

    Publication Year: 2012 , Page(s): 51 - 55
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (195 KB) |  | HTML iconHTML  

    Under reconstructing the operation pattern of traditional economy with information technology, new business rules and competition logic emerge from internet industry, especially the envelopment-competition pattern in e-commerce field. This paper took the envelopment-competition behaviors of Taobao, Baidu, and Tencent as examples, deeply studied the envelopment-competition pattern of E-business platforms. Firstly, the formation mechanism of envelopment phenomenon among the three platforms was analyzed based on current situation. It indicates that user intersection has enormous influence on platform envelopment or being enveloped. Then we analyzed active or passive envelopment-competition behaviors among the three platforms. Finally, the competition trend of E-business platforms was proposed: Taobao, Tencent, and Baidu should expand to peripheral industries on the basis of keeping their core competences, and form a situation of tripartite confrontation. View full abstract»

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  • An Empirical Study of the Influence Factor of Tourism E-Commerce Purchase

    Publication Year: 2012 , Page(s): 56 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (181 KB) |  | HTML iconHTML  

    In order to enhance the industrialization of the tourism e-commerce in china, the author of this paper, adopts SPSS17.0 statistical software to process the hands-on data based on the questionnaires and analyzes the influence factor of the tourism e-commerce systematically. The conclusion is that comprehensive information, secure transaction, convenient communication as well as good after service is the key factors promoting the tourism e-commerce. View full abstract»

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  • Evolution Model and Critical Mass of E-business Platform Based on Complex Networks

    Publication Year: 2012 , Page(s): 60 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (151 KB) |  | HTML iconHTML  

    E-business platform is of typical characteristics of networked market, in which the positive feedback mechanism leads to platform success provided that the number of platform users surpasses its critical mass. Otherwise, the platform would diminish and disappear in the market under the effect of the negative feedback mechanism. This paper, based on the complex network method, developed a dynamical model to describe the number of platform users varying with the time after the platform introduced into market. Then, we analyzed the influence factors and functional mechanisms in determining the critical mass of platform users based on the proposed platform diffusion model. Results show that (1) the platform user network structure and the user decision threshold value are the main factors to determine the critical mass of platform users (2) the heterogeneity of network structure (the standard deviation of degree distributions) is in roughly power function of the critical mass of platform users. View full abstract»

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  • Maximization of Online Display Advertising Slots

    Publication Year: 2012 , Page(s): 65 - 68
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (358 KB) |  | HTML iconHTML  

    Online advertising has become one of the hottest topic in the internet industry because of its great success in creating value to advertisers, web publishers, search engines and consumers. Based on the perishable property of online display advertising spaces, in this paper, it proposes an optimal model about the slot capacity allocation of a certain space and presents some numerical results to support it. It also suggests that web publishers should publish their own advertisings in these slots to market themselves when any type of the time slots isn't sold. View full abstract»

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  • Collaborative Filtering Recommendation Algorithm Based on Item Clustering and Global Similarity

    Publication Year: 2012 , Page(s): 69 - 72
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (170 KB) |  | HTML iconHTML  

    Collaborative filtering is one of the most important algorithms applied in e-commerce recommendation systems. The conventional calculations of similarities are inefficient, which suffers from data sparsity and poor prediction quality problems. In order to overcome the limitations, a new collaborative filtering recommendation algorithm based on item clustering and global similarity is proposed. Firstly, K-MEANS clustering algorithm is applied to cluster items into several classes based on users' ratings on items, and the local user similarity is calculated in each cluster. In addition, the factor of overlap is introduced to optimize the accuracy of the local similarity between users. Finally, a newly global similarity between users is presented to optimize the selection of target user's neighbors and achieve better prediction accuracy. The experimental results show that this method can improve the accuracy of the prediction and enhance the recommendation quality. View full abstract»

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