Jing-Wei Liu

OWA rough set model for forecasting the revenues growth rate of the electronic industry

Business operation performance is related to corporation profitability and directly affects the choices of investment in the stock market. This paper proposes a hybrid method, which combines the ordered weighted averaging (OWA) operator and rough set theory after an attribute selection procedure to deal with multi-attribute forecasting problems with respect to revenue growth rate of the electronic industry. In the attribute selection step, four most-important attributes within 12 attributes collected from related literature are determined via five attribute selection methods as the input of the following procedure of the proposed method. The OWA operator can adjust the weight of an attribute based on the situation of a decision-maker and aggregate different attribute values into a single aggregated value of each instance, and then the single aggregated values are utilized to generate classification rules by rough set for forecasting operation performance. To verify the proposed method, this research collects the financial data of 629 electronic firms for public companies listed in the TSE (Taiwan Stock Exchange) and OTC (Over-the-Counter) market in 2004 and 2005 to forecast the revenue growth rate. The results show that the proposed method outperforms the listing methods.

Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX

In recent years, there have been many time series methods proposed for forecasting enrollments, weather, the economy, population growth, and stock price, etc. However, traditional time series, such as ARIMA, expressed by mathematic equations are unable to be easily understood for stock investors. Besides, fuzzy time series can produce fuzzy rules based on linguistic value, which is more reasonable than mathematic equations for investors. Furthermore, from the literature reviews, two shortcomings are found in fuzzy time series methods: (1) they lack persuasiveness in determining the universe of discourse and the linguistic length of intervals, and (2) only one attribute (closing price) is usually considered in forecasting, not multiple attributes (such as closing price, open price, high price, and low price). Therefore, this paper proposes a multiple attribute fuzzy time series (FTS) method, which incorporates a clustering method and adaptive expectation model, to overcome the shortcomings above. In verification, using actual trading data of the Taiwan Stock Index (TAIEX) as experimental datasets, we evaluate the accuracy of the proposed method and compare the performance with the (Chen, 1996 [7], Yu, 2005 [6], and Cheng, Cheng, & Wang, 2008 [20]) methods. The proposed method is superior to the listing methods based on average error percentage (MAER).

Intelligent preference selection model based on NRE for evaluating student learning achievement

In order to evaluate student learning achievement, several aspects should be considered, such as exercises, examinations, and observations. Traditionally, such an evaluation calculates a final score using a weighted average method after awarding numerical scores, and then determines a grade according to a set of established crisp criteria. However, this approach lacks the potential to reflect the individual characteristics of a class compared to others. Several researches have used fuzzy techniques to devise practical methods for evaluating student learning achievement to ascertain linguistic terms that are usually used by teachers to assess student learning achievement. However, these approaches are largely based on expert opinions and require complicated computational processes. In this paper, we present a new method for evaluating student learning achievement using an adaptive ordered weighted averaging operator and K-nearest-neighbor classification method. The proposed method simulates the evaluation behavior of teachers when performing a student achievement evaluation based on a norm-referenced evaluation by identifying situations involving the application of intelligence and provides a useful means to award a reasonable grade to students. Furthermore, the proposed method provides a feedback mechanism to update the norm dataset. Therefore, the repetitious use of the feedback mechanism will gradually strengthen the representativeness of the norm dataset.

OWA Based Information Fusion Techniques for Classification Problem

In this paper, we fusion multi-attribute data into the aggregated values of single attribute by OWA operators, and cluster the aggregated values for classification tasks. The proposed method is consisted of four steps: (1) use stepwise regression to selection the important attribute, (2) utilize OWA operator to get aggregated values of single attribute from multi-attribute data, (3) cluster the aggregated values by K-Means method, (4) predict the testing datas classes. For verifying, we use two dataset to illustrate the proposed method, and compare with the listing methods. The datasets, one is Iris dataset; the other is Wisconsin-breast-cancer dataset. At last, the result shows that the proposed method is better than the listing methods.

OWA based information fusion method with PCA preprocessing for data classification

Information is getting more and more today, how to handle high dimensions data and high complexity data are the key issues of this contribution. Multi-attribute data usually possesses high data dimension and high data complexity. In order to solve these problems, the contribution proposes a new information fusion model which is briefly described as follows: (1) Reduce data dimensions by principal components analysis (PCA) method. (2) Calculate integrated values by order weighted averaging (OWA) operator. (3) Cluster data instance into specific group and train classification accuracy to obtain the best situation parameter alpha. (4) Validate classification accuracy from testing data. In the research, there are two datasets adopted to verify performances of proposed model, i.e. Iris dataset and Wisconsin Breast Cancer dataset. The experiments results show that classification accuracies of proposed model obviously surpass the listing methods.

OWA Based PCA Information Fusion Method for Classification Problemm

Information plays an important role in modern enterprises, no matter in decision sup- porting and business strategies making all provide efficient supports to executive managers. However, information is getting more and more today, how to handle high dimensions and high complexities data are more difficult than before. This contribution presents a informa-tion fusion method based on the concepts of Ordered Weighted Averaging (OWA) operator and Principal Components Analysis (PCA) method to deal with above problems. The pro-posed method can be described briefly as follows: (1) Reduce data dimensions by PCA method. (2) Calculate integrated values by order OWA operator. (3) Cluster data instances into specific group and train classification accuracies to obtain the best situation parame-ter α. (4) Validate classification accuracies from testing data. This contribution employs five datasets to verify the performances of proposed method and the results of the experi-ments show that the proposed method actually surpasses the listing methods in classification accuracies.

Classifying Cinnamomums using rough sets classifier based on interval-discretization

Classification, which is the task of assigning objects to one of several predefined categories, is a pervasive problem that encompasses many diverse applications. Decision tree classifier, which is a simple yet widely used classification technique, employs training data to yield decision rules; moreover, it can create thresholds and then split the list of continuous attributes into descrete intervals for handling continuous attributes (Quinlan in Journal of Artificial Intelligence Research 4:77–90, 1996). Rough set theory (Pawlak in International Journal of Computer and Information Sciences 11:341–356, 1982; International Journal of Man-Machine Studies 20:469–483, 1984; Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht, 1991) has been applied to a wide variety of decision analysis problems for the extraction of rules from databases. This paper proposes a hybrid approach that takes advantage of combining decision tree and rough sets classifier and applies it to plant classification. The introduced approach starts with decision tree classifier (C4.5) as preprocessing technique to make interval-discretization, subsequently, and uses rough set method for extracting rules. The proposed approach aims at finding out classification rules via analyzing lamina attributes (leaf stalk, leaf width, leaf length, length/width ratio) of Cinnamomum, which are gathered and measured by plant specialists in the field of Taiwan. A comparison with the widely used algorithms (e.g., decision tree, multilayer perceptrons, naïve Bayes, and rough sets classifier) is carried out to show numerous advantages of the proposed approach. Finally, employing with test data in which species are unknown, results of classification are approved by consulting the relative plant specialists.

High-order adaptive model to forecast regional electricity loads

Over the past few years, a considerable number of studies have been proposed on load forecasting. This paper aims at proposing a promising model using high-order adaptive fuzzy time-series algorithm to get more efficient forecasting. From the reviewed literature related to fuzzy time-series, there are two points need to be concerned. The first is to determine a reasonable universe of discourse and the length of intervals, and the second is many researchers ignore the information of trend patterns change in the past history. Hence, this paper utilized the trend weighted and high order adaptive model to deal with above drawbacks. The proposed model is applied for forecasting the regional electricity load in Taiwan. The experiment results showed that the proposed model outperforms the listing methods under MAPE (mean absolute percentage error) criteria.

Multi-attribute Fuzzy Time Series Based on Adaptive Expectation Model

Time-series models have been used to make reasonably accurate predictions in the areas of weather forecasting, academic enrolment and stock price etc...This paper propose a multiple attribute fuzzy time series based on adaptive expectation model, which incorporates trend-weighting and adaptive expectation model into the fuzzy time-series models advanced by Chens and Yu s method. In verification, using actual trading data of Taiwan Stock Index (TAIEX), we evaluate the accuracy of our approach, and compare our proposed method with Chens, Yus and Chengs methods.