Research Article  Open Access
A New DecisionMaking Method for Stock Portfolio Selection Based on Computing with Linguistic Assessment
Abstract
The purpose of stock portfolio selection is how to allocate the capital to a large number of stocks in order to bring a most profitable return for investors. In most of past literatures, experts considered the portfolio of selection problem only based on past crisp or quantitative data. However, many qualitative and quantitative factors will influence the stock portfolio selection in real investment situation. It is very important for experts or decisionmakers to use their experience or knowledge to predict the performance of each stock and make a stock portfolio. Because of the knowledge, experience, and background of each expert are different and vague, different types of 2tuple linguistic variable are suitable used to express experts' opinions for the performance evaluation of each stock with respect to criteria. According to the linguistic evaluations of experts, the linguistic TOPSIS and linguistic ELECTRE methods are combined to present a new decisionmaking method for dealing with stock selection problems in this paper. Once the investment set has been determined, the risk preferences of investor are considered to calculate the investment ratio of each stock in the investment set. Finally, an example is implemented to demonstrate the practicability of the proposed method.
1. Introduction
The purpose of stock portfolio selection is how to allocate the capital to a large number of stocks in order to bring a most profitable return for investors [1]. For this point of view, stock portfolio decision problem can be divided into two questions.
(1)Which stock do you choose?(2)Which investment ratio do you allocate your capital to this stock?There are some literatures to handle the stock portfolio decision problem. Markowitz proposed the meanvariance method for the stock portfolio decision problem in 1952 [2]. In his method, an expected return rate of a bond is treated as a random variable. Stochastic programming is applied to solve the problem. The basic concept of his method can be expressed as follows.
(1)When the risk of stock portfolio is constant, we should pursue to maximize the return rate of stock portfolio.(2)When the return rate of stock portfolio is constant, we should pursue to minimize the risk of stock portfolio.The capital asset pricing model (CAPM), SharpeLintner model, Black model, and twofactor model are derived from the meanvariance method [3, 4]. The capital asset pricing model (CAPM) was developed in 1960s. The concept of the CAPM is that the excepted return rate of the capital with risk is equal to the interest rate of the capital without risk and market risk premium [4]. The methods and theory of the financial decision making can be found in [5–7]. In 1980, Saaty proposed Analytic Hierarchy Process (AHP) to deal with the stock portfolio decision problem by evaluating the performance of each company in different level of criteria [8]. Edirisinghe and Zhang [9] selected the securities by using Data Envelopment Analysis (DEA). Huang [1] defined a new definition of risk and use genetic algorithm to cope with stock portfolio decision problem. Generally, in the portfolio selection problem the decision maker considers simultaneously conflicting objectives such as rate of return, liquidity, and risk. Multiobjective programming techniques such as goal programming (GP) and compromise programming (CP) are used to choose the portfolio [10–12]. Considering the uncertainty of investment environment, Tiryaki transferred experts’ linguistic value into triangle fuzzy number and used a new fuzzy ranking and weighting algorithm to obtain the investment ratio of each stock [4]. In fact, the stock portfolio decision problem can be described as multiple criteria decision making (MCDM) problem.
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is developed by Hwang and Yoon [13], which is one of the wellknown MCDM methods. The basic principle of the TOPSIS method is that the chosen alternative should have the shortest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS). It is an effective method to determine the total ranking order of decision alternatives.
The Elimination et choice in Translating to Reality (ELECTRE) method is a highly developed multicriteria analysis model which takes into account the uncertainty and vagueness in the decision process [14]. It is based on the axiom of partial comparability and it can simplify the evaluation procedure of alternative selection. The ELECTRE method can easily compare the degree of difference among all of alternatives.
In MCDM method, experts can express their opinions by using crisp value, triangle fuzzy numbers, trapezoidal fuzzy numbers, interval numbers, and linguistic variables. Due to imprecise information and experts’ subjective opinion that often appear in stock portfolio decision process, crisp values are inadequate for solving the problems. A more realistic approach may be to use linguistic assessments instead of numerical values [15, 16]. The 2tuple linguistic representation model is based on the concept of symbolic translation [17, 18]. Experts can apply 2tuple linguistic variables to express their opinions and obtain the final evaluation result with appropriate linguistic variable. It is an effective method to reduce the mistakes of information translation and avoid information loss through computing with words [19]. In general, decision makers would use the different 2tuple linguistic variables based on their knowledge or experiences to express their opinions [20]. In this paper, we use different type of 2tuple linguistic variable to express experts’ opinions and combine linguistic ELECTRE method with TOPSIS method to obtain the final investment ratio which is reasonable in real decision environment.
This paper is organized as follows. In Section 2, we present the context of fuzzy set and the definition and operation of 2tuple linguistic variable. In Section 3, we describe the detail of the proposed method. In Section 4, an example is implemented to demonstrate the procedure for the proposed method. Finally, the conclusion is discussed at the end of this paper.
2. The 2Tuple Linguistic Representation
2.1. Fuzzy Set and Triangular Fuzzy Number
Fuzzy set theory is first introduced by Zadeh in 1965 [21]. Fuzzy set theory is a very feasible method to handle the imprecise and uncertain information in a real world [22]. Especially, it is more suitable for subjective judgment and qualitative assessment in the evaluation processes of decision making than other classical evaluation methods applying crisp values [23, 24].
A positive triangular fuzzy number (PTFN) can be defined as , where and , shown in Figure 1. The membership function of positive triangular fuzzy number (PTFN) is defined as [15]
A linguistic variable is a variable whose values are expressed in linguistic terms. In other words, variable whose values are not numbers but words or sentences in a nature or artificial language [25–27]. For example, “weight” is a linguistic variable whose values are very low, low, medium, high, very high, and so forth. These linguistic values can also be represented by fuzzy numbers. There are two advantages for using triangular fuzzy number to express linguistic variable [28]. First, it is a rational and simple method to use triangular fuzzy number to express experts’ opinions. Second, it is easy to do fuzzy arithmetic when using triangular fuzzy number to express the linguistic variable. It is suitable to represent the degree of subjective judgment in qualitative aspect than crisp value.
2.2. The 2Tuple Linguistic Variable
Let be a finite and totally ordered linguistic term set. The number of linguistic term is in set . A 2tuple linguistic variable can be expressed as , where is the central value of th linguistic term in and is a numerical value representing the difference between calculated linguistic term and the closest index label in the initial linguistic term set. The symbolic translation function is presented in [29] to translate crisp value into a 2tuple linguistic variable. Then, the symbolic translation process is applied to translate into a 2tuple linguistic variable. The generalized translation function can be represented as [30]: where , and .
A reverse function is defined to return an equivalent numerical value from 2tuple linguistic information . According to the symbolic translation, an equivalent numerical value is obtained as follow [30]
Let be a 2tuple linguistic variable set. The arithmetic mean is computed as [31] where is the amount of 2tuple linguistic variable. The is a 2tuple linguistic variable which is represented as the arithmetic mean.
In general, decision makers would use the different 2tuple linguistic variables based on their knowledge or experiences to express their opinions [20]. For example, the different types of linguistic variables show as Table 1. Each 2tuple linguistic variable can be represented as a triangle fuzzy number. A transformation function is needed to transfer these 2tuple linguistic variables from different linguistic sets to a standard linguistic set at unique domain. In the method of Herrera and Martinez [29], the domain of the linguistic variables will increase as the number of linguistic variable is increased. To overcome this drawback, a new translation function is applied to transfer a crisp number or 2tuple linguistic variable to a standard linguistic term at the unique domain [30]. Suppose that the interval is the unique domain. The linguistic variable sets with different semantics (or types) will be defined by partitioning the interval . Transforming a crisp number into th linguistic term of type as where , , and is the number of linguistic variable of type .

Transforming th linguistic term of type into a crisp number as where and .
Therefore, the transformation from th linguistic term of type to th linguistic term of type at interval can be expressed as where and .
3. Proposed Method
Because of the knowledge, experience and background of each expert is different and experts’ opinions are usually uncertain and imprecise, it is difficult to use crisp value to express experts’ opinions in the process of evaluating the performance of stock. Instead of crisp value, the 2Tuple linguistic valuable which is an effective method to reduce the mistakes of information translation and avoid information loss through computing with words to express experts’ opinions [19]. In this paper, different types of 2tuple linguistic variables are used to express experts’ opinions.
The TOPSIS method is one of the wellknown MCDM methods. It is an effective method to determine the ranking order of decision alternatives. However, this method cannot distinguish the difference degree between two decision alternatives easily. Based on the axiom of partial comparability, the ELECTRE method can easily compare the degree of difference among of all alternatives. This method always cannot provide the total ordering of all decision alternatives. Therefore, the ELECTRE and TOPSIS methods are combined to determine the final investment ratio.
In the proposed model, the subjective opinions of experts can be expressed by different 2tuple linguistic variables in accordance with their habitual knowledge and experience. After aggregating opinions of all experts, the linguistic TOPSIS and linguistic ELECTRE methods are applied to obtain the investment portfolio sets and , respectively. The strict stock portfolio set is determined by intersection with . In general, the risk preference of investor can be divided into three types such as riskaverter, riskneutral, and riskloving. Considering the risk preference of investor, we can calculate the investment ratio of each stock in strict stock portfolio set . The decision process of the proposed method is shown as in Figure 2.
In general, a stock portfolio decision may be described by means of the following sets:
(i)a set of experts or decisionmakers called ;(ii)a set of stocks called ;(iii)a set of criteria with which stock performances are measured;(iv)a weight vector of each criterion ;(v)a set of performance ratings of each stock with respect to each criterion called .According to the aforementioned description, there are experts, m stocks and criteria in the decision process of stock portfolio. Experts can express their opinions by different 2tuple linguistic variables. The th expert’s opinion about the performance rating of th stock with respect to th criterion can be represented as . The th expert’s opinion about the importance of th criterion can be represented as .
The aggregated linguistic rating of each stock with respect to each criterion can be calculated as
The aggregated linguistic weight of each criterion can be calculated as
3.1. Linguistic TOPSIS Method
Considering the different importance of each criterion, the weighted linguistic decision matrix is constructed as where .
According to the weighted linguistic decision matrix, the linguistic positiveideal solution (LPIS, ) and linguistic negativeideal solution (LNIS, ) can be defined as where and .
The distance of each stock from and can be currently calculated as
A closeness coefficient is defined to determine the ranking order of all stocks once and of each stock have been calculated. The closeness coefficient represents the distances to the linguistic positiveideal solution () and the linguistic negativeideal solution () simultaneously by taking the relative closeness to the linguistic positiveideal solution. The closeness coefficient () of each stock is calculated as
The higher means that stock relatively close to positive ideal solution, the stock has more ability to compete with each others. If the closeness coefficient of stock is greater than the predetermined threshold value , we consider stock is good enough to choose in the investment portfolio set. According to closeness coefficient of each stock, the investment portfolio set can be determined based on investment threshold value as . Finally, the investment ratio of each stock in can be calculated as where is the investment ratio of each stock by linguistic TOPSIS method.
3.2. Linguistic ELECTRE Method
According to the ELECTRE method, the concordance index is calculated for and () with respect to each criterion as where and are indifference and preference threshold values for criterion .
The discordance index is calculated for each pair of stocks with respect to each criterion as where is the veto threshold for criterion .
Calculate the overall concordance index as
The credibility matrix of each pair of the stocks is calculated as where is the set of criteria for which .
The concordance credibility and discordance credibility degrees are defined as [32]
The concordance credibility degree represents that the degree of stock is at least as good as all the other stocks. The discordance credibility degree represents that the degree of all the other stocks is at least as good as stock .
Then, the net credibility degree is defined as . If the net credibility degree of stock is higher, then it represents a higher attractiveness of stock . In order to determine the investment ratio, the outranking index of stock can be defined as
Property 3.1. According to the definition of , we can find .
Proof. Because.
If the stock is better than with respect to each criterion, the best case is
If the stock is worse than with respect to each criterion, the worst case is
Therefore, .
Then, . Finally, we can prove .
The denotes the standardization result of the net credibility degree. According to the definition, it is easy to understand and transform the net credibility degree into interval .
If the outranking index of stock is greater than the predetermined threshold value , we consider stock is good enough to choose in the investment portfolio set. According to the outranking index of each stock, the investment portfolio set can be determined based on investment threshold value as . Finally, the investment ratio of each stock in can be calculated as
where is the investment ratio of each stock by using linguistic ELECTRE method.
3.3. Stock Portfolio Decision
We can consider Linguistic TOPSIS and Linguistic ELECTRE methods as two financial experts to provide investment ratio of each stock, respectively. Smart investor will make a stock portfolio decision by considering the suggestions of investment ratio of each stock simultaneously. Therefore, the portfolio set is defined as strict stock portfolio set .
According to the closeness coefficient, the investment ratio of each stock in strict stock portfolio set can be calculated as According to the outranking index, the investment ratio of each stock in strict stock portfolio set can be calculated as
In general, the investment preference of investors can be divided into three types such as riskaverter (RA), riskneutral (RN), and riskloving (RL). If a person is riskaverter, he/she will consider the smaller investment rates between and . Therefore, the final ratio of each stock in strict portfolio set can be calculated as
If a person is riskneutral, he/she will consider the average investment rates between and . Therefore, the final ratio of each stock in strict portfolio set can be calculated as
If a person is riskloving, he/she will consider the bigger investment rates between and . Therefore, the final ratio of each stock in portfolio set can be calculated as
4. Numerical Example
An example with ten stocks of semiconduct industry in placecountryregion, Taiwan, will be considered to determine the investment ratio of each stock in this paper. Ten stocks are shown as Table 1. A committee of three financial experts has been formed to evaluate the performance of each stock. They are famous professors of a department of finance at wellknown university in countryregionplace, Taiwan. Their knowledge and experiences are enough to evaluate the stock performance of each company for this example. In the process of criteria selection, they considered the quantitative and qualitative factors to deal with the portfolio selection. After the serious discussion and selection by three financial experts, six criteria are considered to determined the investment ratio of each stock such as profitability (), asset utilization (), liquidity (), leverage (), valuation (), growth ().
Profitability ()
The goal of enterprise is tomakeaprofit. There are some indexes to evaluate the profitability of a company such as earnings per share (EPS), net profit margin, return on assets (ROA), and return on equity (ROE). The profitability of a company will influence the performance of each stock.
Asset Utilization ()
Asset utilization means the efficiency of using company’s resource in a period. A good company will promote the resource using efficiency as more as possible. Experts evaluate the asset utilization of the company based on receivables turnover, inventory turnover, and asset turnover.
Liquidity ()
Liquidity will focus on cash flow generation and a company’s ability to meet its financial obligations. When company’s transfer assets (1 and, factory buildings, equipment, patent, goodwill) to currency in a short period, there will have some loss because the company’s manager do not have enough time to find out the buyer who provide the highest price. An appropriate liquidity ratio (debt to equity ratio, current ratio, quick ratio) will both prevent liquidity risk and minimize the working capital.
Leverage ()
When the return on assets is greater than lending rate, it is time for a company to lend money to operate. But increasing the company’s debt will increase risk if the company does not earn enough money to pay the debt in the future. A suitable leverage ratio is one of the criteria to evaluate the performance of each stock.
Valuation ()
Book value means the currency which all of the company’s assets transfer to, stock value means the price if you want to buy now, earnings before amortization, interest and taxes ratio (EBAIT) means the company earns in this year, expert must consider the best time point to buy the stock by Technical Analysis (TA) and Time Series Analysis (TSA). So, valuation is also one of the criteria to evaluate the performance of each stock.
Growth ()
If the scale of a company was expanded year by year, EBAIT will increase which is like “compound interest.” Because of economies of scale, the growth of the company will promote asset utilization and then raise the EBAIT and EPS.
According to the proposed method, the computational procedures of the problem are summarized as follows.
Step 1. Each expert selects the suitable 2tuple linguistic variables to express their opinions. Expert 1 uses linguistic variables with 5 scale of linguistic term set to express his opinion, expert 2 uses linguistic variables with 7 scale of linguistic term set and expert 3 uses linguistic variables with 9 scale of linguistic term set, respectively (see Table 2).

Step 2. Each expert expresses his opinion about the performance of each stock with respect to each criterion as shown in Table 3.

Step 3. Each expert expresses his opinion about the importance of each criterion as shown in Table 4.

Step 4. Transform the linguistic ratings into the linguistic variables of type 2 and aggregate the linguistic ratings of each stock with respect to criteria as Table 5.

Step 5. Transform the linguistic evaluations of weight of each criterion into the linguistic variables of type 2 and aggregate the linguistic weight of each criterion as Table 6.

Step 6. Calculate the weighted linguistic decision matrix as Table 7.

Step 7. Calculate the linguistic positiveideal solution (LPIS, ) and linguistic negativeideal solution (LNIS, ) as Table 8.

Step 8. Calculate the distance of each stock from and the distance from , and the closeness coefficient of each stock as Table 9.

Step 9. Define investment threshold value as the average of the closeness coefficient , so the investment portfolio set is in accordance with TOPSIS. The ratio of investment based on TOPSIS method is shown as Table 13.
Step 10. The indifference threshold, preference threshold, and veto threshold values of each criterion can be determined in accordance with the linguistic variables of type 2 as
Step 11. Calculate the concordance matrix and the discordance matrix of each pair stock with respect to each criterion.Then, calculate the overall concordance matrix as Table 10 and the credibility matrix as Table 11.


Step 12. Calculate the concordance credibility degree, the discordance credibility degree, the net credibility degree, and the outranking index as Table 12.


Step 13. Define investment threshold value as the average of the outranking index , so the investment portfolio set is in accordance with ELECTRE method. The ratio of investment based on ELECTRE method is shown as Table 13.
Step 14. Compute strict stock portfolio set as .
Step 15. According to the investment preference of investor, the result of the ratio of investment based on combining linguistic ELECTRE with TOPSIS can be calculated as Table 13.
According to the result of numerical example, experts considered that the proposed method is useful to help investor determine the stock portfolio.
5. Conclusion
In general, the stock portfolio decision problem adheres to uncertain and imprecise data, and fuzzy set theory is adequate to deal with it. In this proposed model, different types of 2tuple linguistic variables are applied to express the subjective judgment of each expert. Expert can easily express his opinion by different types of 2tuple linguistic variables. The generalized translation method of different types of 2tuple linguistic variables is applied to aggregate the subjective judgment of each expert. It is a flexible way to aggregate the opinions of all experts. Then, a new decisionmaking method has been presented in this paper by combining the advantages of ELECTRE with TOPSIS methods. According to the experts’ opinions, the linguistic ELECTRE method and linguistic TOPSIS method are used to derive the closeness coefficient and the outranking index of each stock, respectively. Based on the closeness coefficient, the outranking index, and selection threshold, we can easily obtain three type of the investment ratio in accordance with different investment preference of final decisionmaker. It is a reasonable way in real decision environment. In other words, the proposed method provides a flexible way to determine the stock portfolio under the uncertain environment. In the future, the concept of combing different decision methods for deciding stock portfolio will be applied to different fields such as R&D projects investment, bonus distribution in a company. A decision support system will be developed based on the proposed method for dealing with the stock selection problems in the future.
References
 X. Huang, “Portfolio selection with a new definition of risk,” European Journal of Operational Research, vol. 186, no. 1, pp. 351–357, 2008. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 H. Markowitz, “Portfolio selection,” Journal of Finance, pp. 77–91, 1952. View at: Google Scholar
 C. A. Magni, “Correct or incorrect application of CAPM? Correct or incorrect decisions with CAPM?,” European Journal of Operational Research, vol. 192, no. 2, pp. 549–560, 2009. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 F. Tiryaki and M. Ahlatcioglu, “Fuzzy stock selection using a new fuzzy ranking and weighting algorithm,” Applied Mathematics and Computation, vol. 170, no. 1, pp. 144–157, 2005. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 E. F. Fama and K. R. French, “The crosssection of expected stock returns,” Journal of Finance, vol. 47, pp. 427–465, 1992. View at: Google Scholar
 C. D. Feinstein and M. N. Thapa, “Notes: a reformulation of a meanabsolute deviation portfolio optimization model,” Management Sciences, vol. 39, no. 12, pp. 1552–1553, 1993. View at: Publisher Site  Google Scholar
 W. F. Sharpe, G. J. Alexander, and J. F. Bailey, Investments, PrenticeHall, Englewood Cliffs, NJ, USA, 6th edition, 1999.
 T. L. Saaty, P. C. Rogers, and R. Bell, “Portfolio selection through hierarchies,” Journal of Portfolio Manage, pp. 16–21, 1980. View at: Google Scholar
 N. C. P. Edirisinghe and X. Zhang, “Generalized DEA model of fundamental analysis and its application to portfolio optimization,” Journal of Banking & Finance, vol. 31, no. 11, pp. 3311–3335, 2007. View at: Publisher Site  Google Scholar
 F. Ben Abdelaziz, B. Aouni, and R. El Fayedh, “Multiobjective stochastic programming for portfolio selection,” European Journal of Operational Research, vol. 177, no. 3, pp. 1811–1823, 2007. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 E. Ballestero, “Stochastic goal programming: a meanvariance approach,” European Journal of Operational Research, vol. 131, no. 3, pp. 476–481, 2001. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 B. Aouni, F. Ben Abdelaziz, and J.M. Martel, “Decisionmaker's preferences modeling in the stochastic goal programming,” European Journal of Operational Research, vol. 162, no. 3, pp. 610–618, 2005. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 C. L. Hwang and K. Yoon, Multiple Attribute Decision Making Methods and Applications, vol. 186 of Lecture Notes in Economics and Mathematical Systems, Springer, Berlin, Germany, 1981. View at: MathSciNet
 A. Papadopoulos and A. Karagiannidis, “Application of the multicriteria analysis method Electre III for the optimisation of decentralised energy systems,” Omega, vol. 36, no. 5, pp. 766–776, 2008. View at: Publisher Site  Google Scholar
 C.T. Chen, “Extensions of the TOPSIS for group decisionmaking under fuzzy environment,” Fuzzy Sets and Systems, vol. 114, no. 1, pp. 1–9, 2000. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 F. Herrera and E. HerreraViedma, “Linguistic decision analysis: steps for solving decision problems under linguistic information,” Fuzzy Sets and Systems, vol. 115, no. 1, pp. 67–82, 2000. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 F. Herrera and L. Martinez, “A 2tuple fuzzy linguistic representation model for computing with words,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 6, pp. 746–752, 2000. View at: Publisher Site  Google Scholar
 Z. Xu, “Deviation measures of linguistic preference relations in group decision making,” Omega, vol. 33, no. 3, pp. 249–254, 2005. View at: Publisher Site  Google Scholar
 E. HerreraViedma, O. Cordón, M. Luque, A. G. Lopez, and A. M. Muñoz, “A model of fuzzy linguistic IRS based on multigranular linguistic information,” International Journal of Approximate Reasoning, vol. 34, no. 23, pp. 221–239, 2003. View at: Publisher Site  Google Scholar  MathSciNet
 F. Herrera, L. Martinez, and P. J. Sanchez, “Managing nonhomogeneous information in group decision making,” European Journal of Operational Research, vol. 166, no. 1, pp. 115–132, 2005. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 R. R. Yager, “An approach to ordinal decision making,” International Journal of Approximate Reasoning, vol. 12, no. 34, pp. 237–261, 1995. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 C.T. Lin and Y.T. Chen, “Bid/nobid decisionmaking—a fuzzy linguistic approach,” International Journal of Project Management, vol. 22, no. 7, pp. 585–593, 2004. View at: Publisher Site  Google Scholar
 R.C. Wang and S.J. Chuu, “Group decisionmaking using a fuzzy linguistic approach for evaluating the flexibility in a manufacturing system,” European Journal of Operational Research, vol. 154, no. 3, pp. 563–572, 2004. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 E. HerreraViedma and E. Peis, “Evaluating the informative quality of documents in SGML format from judgements by means of fuzzy linguistic techniques based on computing with words,” Information Processing and Management, vol. 39, no. 2, pp. 233–249, 2003. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—I,” Information Sciences, vol. 8, pp. 199–249, 1975. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—II,” Information Sciences, vol. 8, pp. 301–357, 1975. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 A. Kaufmann and M. M. Gupta, Introduction to Fuzzy Arithmetic: Theory and Applications, Van Nostrand Reinhold, New York, NY, USA, 1991. View at: MathSciNet
 F. Herrera and L. Martinez, “A model based on linguistic 2tuples for dealing with multigranular hierarchical linguistic contexts in multiexpert decisionmaking,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 31, no. 2, pp. 227–234, 2001. View at: Publisher Site  Google Scholar
 W.S. Tai and C.T. Chen, “A new evaluation model for intellectual capital based on computing with linguistic variable,” Expert Systems with Applications, vol. 36, no. 2, pp. 3483–3488, 2009. View at: Publisher Site  Google Scholar
 E. HerreraViedma, F. Herrera, L. Martínez, J. C. Herrera, and A. G. López, “Incorporating filtering techniques in a fuzzy linguistic multiagent model for information gathering on the web,” Fuzzy Sets and Systems, vol. 148, no. 1, pp. 61–83, 2004. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 H.F. Li and J.J. Wang, “An improved ranking method for ELECTRE III,” in International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM '07), pp. 6659–6662, 2007. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2009 ChenTung Chen and WeiZhan Hung. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.