Browsing by Author "Ong, CS"

Now showing 1 - 11 of 11

A novel algorithm for dynamic factor analysis
(2006-04-15)
Huang, JJ
;
Tzeng, GH
;
Ong, CS
;
科技管理研究所
;
Institute of Management of Technology
In this paper, a dynamic factor model is proposed to extract the dynamic factors from time series data. In order to deal with the problem of scaling, the cross-correlation matrices (CCM) are first employed to cluster the time series data. Then, the dynamic factors are extracted using the revised independent component analysis (ICA). In addition, a numerical study is used to demonstrate the proposed method. On the basis of the simulated results, we can conclude that the proposed method can really extract the effective dynamic factors. (c) 2005 Elsevier Inc. All rights reserved.
A novel algorithm for uncertain portfolio selection
(2006-02-01)
Huang, HJ
;
Tzeng, GH
;
Ong, CS
;
運輸與物流管理系註：原交通所+運管所
;
科技管理研究所
In this paper, the conventional mean-variance method is revised to determine the optimal portfolio selection under the uncertain situation. The possibilistic area of the return rate is first derived using the possibisitic regression model. Then, the Mellin transformation is employed to obtain the mean and the risk by considering the uncertainty. Next, the revised mean-variance model is proposed to deal with the problem of uncertain portfolio selection. In addition, a numerical example is used to demonstrate the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide the more flexible and accurate results than the conventional method under the uncertain portfolio selection situation. (c) 2005 Elsevier Inc. All rights reserved.
A novel hybrid model for portfolio selection
(2005-10-15)
Ong, CS
;
Huang, HJ
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
As we know, the performance of the mean-variance approach depends on the accurate forecast of the return rate. However, the conventional method (e.g. arithmetic mean or regression-based method) usually cannot obtain a satisfied solution especially under the small sample situation. In this paper, the proposed method which incorporates the grey and possibilistic regression models formulates the novel portfolio selection model. In order to solve the multi-objective quadric programming problem, multi-objective evolution algorithms (MOEA) is employed. A numerical example is also illustrated to show the procedures of the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide the more flexible and accurate results. (c) 2004 Elsevier Inc. All rights reserved.
Building credit scoring models using genetic programming
(2005-07-01)
Ong, CS
;
Huang, JJ
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed to significantly improving the accuracy of the credit scoring mode. In this paper, genetic programming (GP) is used to build credit scoring models. Two numerical examples will be employed here to compare the error rate to other credit scoring models including the ANN, decision trees, rough sets, and logistic regression. On the basis of the results, we can conclude that GP can provide better performance than other models. (c) 2005 Elsevier Ltd. All rights reserved.
Fuzzy principal component regression (FPCR) for fuzzy input and output data
(2006-02-01)
Huang, JJ
;
Tzeng, GH
;
Ong, CS
;
科技管理研究所
;
Institute of Management of Technology
Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollinearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regression model. In addition, a numerical example is used to demonstrate the proposed method and compare with other methods. On the basis of the results, we can conclude that the proposed method can provide a correct fuzzy regression model and avoid the problem of multicollinearity.
Interval multidimensional scaling for group decision using rough set concept
(2006-10-01)
Huang, JJ
;
Ong, CS
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
Multidimensional scaling (MDS) is a statistical tool for constructing a low-dimension configuration to represent the relationships among objects. In order to extend the conventional MDS analysis to consider the situation of uncertainty under group decision making, in this paper the interval-valued data is considered to represent the dissimilarity matrix in MDS and the rough sets concept is used for dealing with the problems of group decision making and uncertainty simultaneously. In addition, two numerical examples are used to demonstrate the proposed method in both the situation of individual differences scaling and the conventional MDS analysis with the interval-valued data, respectively. On the basis of the results, we can conclude that the proposed method is more suitable for the real-world problems. (c) 2005 Elsevier Ltd. All rights reserved.
Model identification of ARIMA family using genetic algorithms
(2005-05-25)
Ong, CS
;
Huang, JJ
;
Tzeng, GH
;
運輸與物流管理系註：原交通所+運管所
;
科技管理研究所
ARIMA is a popular method to analyze stationary univariate time series data. There are usually three main stages to build an ARIMA model, including model identification, model estimation and model checking, of which model identification is the most crucial stage in building ARIMA models. However there is no method suitable for both ARIMA and SARIMA that can overcome the problem of local optima. In this paper, we provide a genetic algorithms (GA) based model identification to overcome the problem of local optima, which is suitable for any ARIMA model. Three examples of times series data sets are used for testing the effectiveness of GA, together with a real case of DRAM price forecasting to illustrate an application in the semiconductor industry. The results show that the GA-based model identification method can present better solutions, and is suitable for any ARIMA models. (c) 2004 Elsevier Inc. All rights reserved.
Motivation and resource-allocation for strategic alliances through the DeNovo perspective
(2005-03-01)
Huang, JJ
;
Tzeng, GH
;
Ong, CS
;
運輸與物流管理系註：原交通所+運管所
;
科技管理研究所
In recent years, there have been proposed many theories and models, such as transaction cost theory and the resource-based view, to explain the formation of strategic alliances. However, the perspectives are usually limited and incomplete. Additionally, the problem of resource allocation is also a serious issue when firms enter strategic alliances. This paper proposes a holistic perspective and provides an optimal resource portfolio by using the DeNovo perspective. A numerical example demonstrates the criteria of strategic alliances and provides the optimal resource portfolio. (c) 2005 Elsevier Ltd. All rights reserved.
Multidimensional data in multidimensional scaling using the analytic network process
(2005-05-01)
Huang, JJ
;
Tzeng, GH
;
Ong, CS
;
科技管理研究所
;
Institute of Management of Technology
Multidimensional scaling (MDS) is a statistical tool for constructing a low-dimensional configuration to represent the relationships between the objects. Although MDS has been widely used in various fields, it is difficult to evaluate similarity/ dissimilarity between the complex systems by human judgment. Even though we can divide a complex system into subsystems, which can be more easily evaluated, the relative weights of the subsystems are also a crucial problem. Because of these subsystems usually exist interdependence and feedback, the weights of the subsystems are hard to obtain. This paper proposes a method which combines the methods of the interpretive structural modeling (ISM) and the analytic network process (ANP) procedures to deal with the problem of the subsystems' interdependence and feedback. In addition, we also provide a numerical example to illustrate the proposed method. (c) 2004 Elsevier B.V. All rights reserved.
Optimal fuzzy multi-criteria expansion of competence sets using multi-objectives evolutionary algorithms
(2006-05-01)
Huang, JJ
;
Tzeng, GH
;
Ong, CS
;
科技管理研究所
;
Institute of Management of Technology
Competence set is widely used to plan the optimal expansion process of skills, abilities or strategies. However. the conventional method is concerned only with one criterion rather than multi-criteria problems. In addition, the crisp value cannot reflect the ambiguity and the uncertainty in practice. In this paper, we propose the fuzzy criteria competence set analysis. In order to obtain Pareto solutions, multi-objective evolutionary algorithm (MOEA) is employed here. A numerical example with two fuzzy criteria is also used to illustrate the proposed method. (c) 2005 Elsevier Ltd. All rights reserved.
Two-stage genetic programming (2SGP) for the credit scoring model
(2006-03-15)
Huang, JJ
;
Tzeng, GH
;
Ong, CS
;
運輸與物流管理系註：原交通所+運管所
;
科技管理研究所
Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF-THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models. (c) 2005 Published by Elsevier Inc.