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In the analysis of predicting financial distress based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone...
Information systems outsourcing has been one of the critical issues in information systems management. Various strategies to IS outsourcing have emerged. Although many articles have appeared on outsourcing, few have extended the discussion beyond simple cost and benefit analysis. Vendor selection is a difficult problem which includes both tangible and intangible factors. Until now, there are no effective...
Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicantpsilas credit score from the applicantpsilas...
Stock return forecast has been an important issue and difficult task for both shareholders and financial professionals. To tackle this problem, we introduce least square support vector machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and dynamic inertia weight particle swarm optimization (W-PSO), that outperform standard PSO in parameter selection. The work of this...
The evaluation of competitive power is very important for bidder in power system, how to improve the accuracy and efficiency of evaluation is the keystone people pay attention to, and many researches have been done around it. A combined model of least squares support vector machines optimized by an improved particle swarm optimization algorithm is proposed in this paper to do evaluate the competitive...
[Purpose] Mass incidents have emerged as a serious social problem concerning national security in China. So, it is necessary to construct a forecasting model to predict such public events. In this paper, support vector machines are applied to the model. [Method] Based on the social surveys conducted in 119 counties of Shanxi, Gansu and Hubei provinces, 3 multi-class classification problems were proposed,...
In this paper, we use a group decision-making (GDM) approach based on the variable precision rough set (VPRS) model to evaluate partner relationship in the value network of a mobile portal. We extend the existing study on VPRS-based GDM where the optimal betak-stable interval is derived for DMk [1]. Firstly, in the value network of a mobile portal, VPRS-based GDM is used to evaluate relationship between...
In order to realize evaluation of oil storage safety, clear the redundant data in indexes membership which is not useful for goal classification, based on data mining of entropy, mining knowledge information about object classification hidden in every index, affirming the relation of object classification and index membership, eliminating the redundant data in index membership for object classification...
This paper presents a new method (fuzzy pay-off method) for real option valuation using fuzzy numbers that is based on findings from earlier real option valuation methods and from fuzzy real option valuation. The method is intuitive to understand and far less complicated than any previous real option valuation model to date. The paper also presents the use of triangular and trapezoidal fuzzy numbers...
Existing clustering ensemble algorithms for partitioning categorical data only apply to know the generating process of clustering members very well. In order to broaden the application of clustering ensemble, a fuzzy clustering ensemble algorithm for partitioning categorical data is proposed in this paper. The proposed algorithm makes use of relationship degree between different attributes for pruning...
In order to find an effective method to deal with the uncertainty of the risk evaluation of real estate investment, a new method based on the AHM (Analytic hierarchical model) and the fuzzy comprehensive evaluation was proposed. After the establishment of the evaluation system, the AHM was employed to determine the weight of every index, and the value of the risk was obtained by using the fuzzy set...
This paper proposes a method for solving fuzzy linear programming problems where all the coefficients are fuzzy numbers. Firstly, this paper use the expected value of fuzzy variable presented by Baoding Liu to compare two fuzzy numbers. Secondly, this paper offer the acceptable solution of fuzzy linear programming for the decision-maker in different degrees of feasiblity. Lastly, we solve one numerical...
Based on chaos theory in economics, this paper introduces an application of the non-linear Duffing equation in financial crisis and briefly interprets an equation coefficient corresponding to the economic significance. This paper specifically focuses on the empirical problems of the Duffing equation in financial markets to solve the anti-disturbance coefficient of financial system to the external...
In order to improve efficiency of face detector, fuzzy set theory is used in establishing distribution face detector. This detector trains the sample set by Haar-like feature and membership function, and selects appropriate weak classifiers through the feature setpsilas entropy and AdaBoost learning algorithm. Subsequently distribution face detector is established and tested on the MIT+CMU frontal...
In view of fuzziness of university teaching quality evaluation, AHP-based multi-level fuzzy comprehensive evaluation model become the advantage model for university teaching quality assessment. However, membership conversion way at present has a problem of redundancy, that is, the redundant data which has no value to objective classification in memberships of indicators is used to calculate the objective...
In order to solve the problem of user-classification to reflect the features of Web users inflexible, a novel user classification model was presented in this paper. By introducing the concept of time discretization and applying fuzzy equivalence relation clustering to classify Web users, the model can rationally solve the user classification problems. Empirical results showed that the output of user...
In this paper, mixture models are used to classify documents. The basic assumption for the documents in a collection is that each class is composed of a number of mixture components. By identifying the components in the document collection, the classes of documents can thereby be identified from each other. A semi-supervised clustering method is proposed to identify the components (clusters), and...
In order to improve the precise rate and recall rate of Chinese text classifier, an improved bagging algorithm - attribute bagging is used in this paper. Document is represented by vector space model and information gain is used to do the feature selection. Re-sampling attributes is used to get multiple training sets and the kNN is selected as the individual classifier. The classification result is...
To the best of our knowledge, the problem of mining multi-relational frequent patterns in data streams is still unsolved up to now. To attack this problem, an algorithm RFPS, which is based on novel data synopsis and declarative bias, is proposed in this paper. By introducing a new data synopsis method, where period sampling is used, many samplespsila checking operations are avoided. Meanwhile, lots...
In order to solve the problems that there is less valid information on the vehicle maintenance and the intelligent fault diagnosis system is inefficient with increasing the case base, based on case based reasoning (CBR). Extenics is used in designing case base. Firstly, extension model and binary tree are used for the formal description, as the mode of knowledge representation. Secondly, extension...
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