Ching-Hsue Cheng

Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly

Abstract Fault-tree analysis (FTA) is a powerful technique used to identify the root causes of undesired event in system failure by constructing a tree of sub-events, spreading into bottom events, procreating the fault and finally heading to the top event. From integrating expert’s knowledge and experience in terms of providing the possibilities of failure of bottom events, an algorithm of the intuitionistic fuzzy fault-tree analysis is proposed in this paper to calculate fault interval of system components and to find the most critical system component for the managerial decision-making based on some basic definitions. The proposed method is applied for the failure analysis problem of printed circuit board assembly (PCBA) to generate the PCBA fault-tree, fault-tree nodes, then directly compute the intuitionistic fuzzy fault-tree interval, traditional reliability, and the intuitionistic fuzzy reliability interval. The result of this proposed method is compared with the existing approaches of fault-tree methods.

Classifying the segmentation of customer value via RFM model and RS theory

Data mining is a powerful new technique to help companies mining the patterns and trends in their customers data, then to drive improved customer relationships, and it is one of well-known tools given to customer relationship management (CRM). However, there are some drawbacks for data mining tool, such as neural networks has long training times and genetic algorithm is brute computing method. This study proposes a new procedure, joining quantitative value of RFM attributes and K-means algorithm into rough set theory (RS theory), to extract meaning rules, and it can effectively improve these drawbacks. Three purposes involved in this study in the following: (1) discretize continuous attributes to enhance the rough sets algorithm; (2) cluster customer value as output (customer loyalty) that is partitioned into 3, 5 and 7 classes based on subjective view, then see which class is the best in accuracy rate; and (3) find out the characteristic of customer in order to strengthen CRM. A practical collected C-company dataset in Taiwans electronic industry is employed in empirical case study to illustrate the proposed procedure. Referring to [Hughes, A. M. (1994). Strategic database marketing. Chicago: Probus Publishing Company], this study firstly utilizes RFM model to yield quantitative value as input attributes; next, uses K-means algorithm to cluster customer value; finally, employs rough sets (the LEM2 algorithm) to mine classification rules that help enterprises driving an excellent CRM. In analysis of the empirical results, the proposed procedure outperforms the methods listed in terms of accuracy rate regardless of 3, 5 and 7 classes on output, and generates understandable decision rules.

Fuzzy time-series based on adaptive expectation model for TAIEX forecasting

Time-series models have been used to make predictions in the areas of stock price forecasting, academic enrollment and weather, etc. However, in stock markets, reasonable investors will modify their forecasts based on recent forecasting errors. Therefore, we propose a new fuzzy time-series model which incorporates the adaptive expectation model into forecasting processes to modify forecasting errors. Using actual trading data from Taiwan Stock Index (TAIEX) and, we evaluate the accuracy of the proposed model by comparing our forecasts with those derived from Chens [Chen, S. M. (1996). Forecasting enrollments based on fuzzy time-series, Fuzzy Sets and Systems, 81, 311-319] and Yus [Yu, Hui-Kuang. (2004). Weighted fuzzy time-series models for TAIEX forecasting. Physica A, 349, 609-624] models. The comparison results indicate that our model surpasses in accuracy those suggested by Chen and Yu.

A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting

In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized. In order to overcome these drawbacks, this paper proposes a hybrid forecasting model, using multi-technical indicators to predict stock price trends. Further, it includes four proposed procedures in the hybrid model to provide efficient rules for forecasting, which are evolved from the extracted rules with high support value, by using the toolset based on rough sets theory (RST): (1) select the essential technical indicators, which are highly related to the future stock price, from the popular indicators based on a correlation matrix; (2) use the cumulative probability distribution approach (CDPA) and minimize the entropy principle approach (MEPA) to partition technical indicator value and daily price fluctuation into linguistic values, based on the characteristics of the data distribution; (3) employ a RST algorithm to extract linguistic rules from the linguistic technical indicator dataset; and (4) utilize genetic algorithms (GAs) to refine the extracted rules to get better forecasting accuracy and stock return. The effectiveness of the proposed model is verified with two types of performance evaluations, accuracy and stock return, and by using a six-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) as the experiment dataset. The experimental results show that the proposed model is superior to the two listed forecasting models (RST and GAs) in terms of accuracy, and the stock return evaluations have revealed that the profits produced by the proposed model are higher than the three listed models (Buy-and-Hold, RST and GAs).

Multi-attribute fuzzy time series method based on fuzzy clustering

Traditional time series methods can predict the seasonal problem, but fail to forecast the problems with linguistic value. An alternative forecasting method such as fuzzy time series is utilized to deal with these kinds of problems. Two shortcomings of the existing fuzzy time series forecasting methods are that they lack persuasiveness in determining universe of discourse and the length of intervals, and that they lack objective method for multiple-attribute fuzzy time series. This paper introduces a novel multiple-attribute fuzzy time series method based on fuzzy clustering. The methods of fuzzy clustering are integrated in the processes of fuzzy time series to partition datasets objectively and enable processing of multiple attributes. For verification, this paper uses two datasets: (1) the yearly data on enrollments at the University of Alabama, and (2) the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) futures. The forecasting results show that the proposed method can forecast not only one-attribute but also multiple-attribute data effectively and outperform the listing methods.

Selecting IS personnel use fuzzy GDSS based on metric distance method

Abstract In this paper we propose a new approach to rank fuzzy numbers by metric distance. For showing our method is a good ranking method, we give two examples to compare with other methods. The paper also developes a computer-based group decision support system, FMCGDSS, to increase the recruiting productivity and to easily compare our method with other fuzzy number ranking methods. The FMCGDSS includes three ranking methods: intuition ranking, Lee and Lis fuzzy mean/spread and our metric distance method to help manager make better decision under fuzzy circumstance. The result indicates that the new method is coincident with the intuition ranking and the Lee and Lis fuzzy mean/spread method on each type weight.

Trend-weighted fuzzy time-series model for TAIEX forecasting

Time-series models have been used to make reasonably accurate predictions in the areas of weather forecasting, academic enrolment and stock price etc... We propose a methodology which incorporates trend-weighting into the fuzzy time-series models advanced by S.M. Chen and Hui-Kuang Yu. By using actual trading data of Taiwan Stock Index (TAIEX) and the enrolment data of the University of Alabama, we evaluate the accuracy of our trend-weighted, fuzzy, time-series model by comparing our forecasts with those derived from Chens and Yus models. The results indicate that our model surpasses in accuracy those suggested by Chen and Yu.

A risk assessment methodology using intuitionistic fuzzy set in FMEA

Most current risk assessment methods use the risk priority number (RPN) value to evaluate the risk of failure. However, conventional RPN methodology has been criticised as having five main shortcomings as follows: (1) the assumption that the RPN elements are equally weighted leads to over simplification; (2) the RPN scale itself has some non-intuitive statistical properties; (3) the RPN elements have many duplicate numbers; (4) the RPN is derived from only three factors mainly in terms of safety; and (5) the conventional RPN method has not considered indirect relations between components. To address the above issues, an efficient and comprehensive algorithm to evaluate the risk of failure is needed. This article proposes an innovative approach, which integrates the intuitionistic fuzzy set (IFS) and the decision-making trial and evaluation laboratory (DEMATEL) approach on risk assessment. The proposed approach resolves some of the shortcomings of the conventional RPN method. A case study, which assesses the risk of 0.15 µm DRAM etching process, is used to demonstrate the effectiveness of the proposed approach. Finally, the result of the proposed method is compared with the listing approaches of risk assessment methods.

Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets

This study proposes a hybrid fuzzy time series model with two advanced methods, cumulative probability distribution approach (CPDA) and rough set rule induction, to forecast stock markets. To improve forecasting accuracy, three refining processes of fuzzy time series are provided in the proposed model: (1) using CPDA to discretize the observations in training datasets based on the characteristics of data distribution, (2) generating rules (fuzzy logical relationships) by rough set algorithm and (3) producing forecasting results based on rule support values from rough set algorithm. To verify the forecasting performance of the proposed model in detail, two empirical stock markets (TAIEX and NYSE) are used as evaluating databases; two other methodologies, proposed by Chen and Yu, are used as comparison models, and two different evaluation methods (moving windows) are used. The proposed model shows a greatly improved performance in stock market forecasting compared to other fuzzy time series models.

Entropy-based fuzzy rough classification approach for extracting classification rules

Abstract Recently, the combination of Fuzzy Set Theory and Rough Set Theory has become a popular data mining technique for classification problems because of their strength of handling vague and imprecise data. From the previous literature, Rough Set Theory can only operate effectively with datasets containing discrete values. As most datasets contain real-valued features, it is necessary to perform a discretization step beforehand, which is typically implemented by standard fuzzification techniques. In this paper, a new fuzzification technique called Modified Minimization Entropy Principle Algorithm (MMEPA) is proposed to construct membership functions of fuzzy sets of linguistic variables. Using the dataset fuzzified by this technique to perform the rule extraction algorithm Variable Precision Rough Set Model (VP-model), the extracted classification rules by this model can obtain a higher classification accuracy rate than that of some existing methods.