Jia-Wen Wang

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.

OWA-weighted based clustering method for classification problem

Information classification is an important role in decision-making problems. As information technology advances, large amounts of information stored in database. Many tasks are worked out in high complexity and dimensionality in classification problem. Therefore, the paper applies ordered weighted averaging (OWA) operator to fusion multi-attribute data into the aggregated values of single attribute, and cluster the aggregated values for classification tasks. The proposed method consists of four steps: (1) use stepwise regression to select and order 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 clusters of testing data. In verification and comparison, three datasets: (1) Iris, (2) Wisconsin-breast-cancer, and (3) Key Performance Indicators datasets are conducted by the proposed method. The problems of high complexity and dimensionality are solved and the classification accuracy rate is higher than some existing methods.

Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix

Forecasting the number of outpatient visits can help the expert of healthcare administration to make a strategic decision. If the number of outpatient visits could be forecast accurately, it would provide the administrators of healthcare with a basis to manage hospitals effectively, to make up a schedule for human resources and finances reasonably, and distribute hospital material resources suitably. This paper proposes a new fuzzy time series method, which is based on weighted-transitional matrix, also proposes two new forecasting methods: the Expectation Method and the Grade-Selection Method. From the verification and results, the proposed methods exhibit a relatively lower error rate in comparison to the listing methods, and could be more stable in facing the ever-changing future trends. The characteristics of the proposed methods could overcome the drawback of the insufficient handling of information to construct a forecasting rule in previous researches.

Flexible fuzzy OWA querying method for hemodialysis database

The hemodialysis quality contains the subjective opinions of the physicians. However, the range of good/bad quality of one physician’s perspective usually differs from the others, so we use the fuzzy theory to solve this vague situation. This paper proposes the fuzzy ordered weighting average (OWA) technique to evaluate fuzzy database queries about linguistic or precise values, which can improve the crisp values’ constrains of traditional database. Besides, we deal with the dynamical weighting problem more rationally and flexibly according to the situational parameter α value from the user’s viewpoint. In this paper, we focus on hemodialysis adequacy and develop the query system of practical hemodialysis database for a regional hospital in Taiwan. From the experimental result, we can find the overall accuracy rate is better than other methods and our result is more matching the doctor’s view. That is, the fuzzy OWA query is more flexible and more accurate

A new approach for estimating null value in relational database

In general, a database system will not operate properly if it exist some null values of attributes in the system. In this paper, we propose a new approach to estimate null values in relational database, which utilize other clustering algorithm to cluster data, and use fuzzy correlation and distance similarity to calculate the correlation of different attribute. For verifying our method, this paper utilize mean of absolute error rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves importance than the existing methods for estimating null values in relational database systems.

Information Fusion Technique for Weighted Time Series Model

In this paper, we propose an information fusion technique for weighted time series model, is called OWA-MA forecasting model. The OWA-MA forecasting model combines OWA operator and weighted moving average (WMA). The model deals with the dynamical weighting problem more rationally and flexibly according to the situational parameter alpha value from the users viewpoint. For verifying proposed method, we use two datasets to illustrate our performance, the datasets are: dataset 1 - the yearly data on enrollments at the university of Alabama and dataset 2 - the forecast demand table to evaluate the proposed model. Furthermore, the tracking signal as evaluation criteria to compares the proposed model with other models. It is shown that our proposed method proves better than other methods for time series model.

Constructing New Drug Adoption Rules in Medical Institution

Knowledge of medicinal chemical compounds is complex and evidence-based. This study extracted knowledge of drugs from experts and assessed that knowledge using sophisticated computational techniques for critical decision making. By using fuzzy inference system this study evaluated new drug candidates in efficient way by considering not only safety and efficacy, but also the hottest issue-cost. The process utilized in this study was based on the consensus of the Drugs and Therapeutic Committee (DTC). Finally, this study establishes new drug adoption rules based on the knowledge of DTC members and implements a fuzzy inference system. Notably, the verification and comparison of this study also provide an intuitive direction for system development and further researches.

An efficient method for estimating null values in relational databases

Generally, a database system containing null value attributes will not operate properly. This study proposes an efficient and systematic approach for estimating null values in a relational database which utilizes clustering algorithms to cluster data, and a regression coefficient to determine the degree of influence between different attributes. Two databases are used to verify the proposed method: (1) Human resource database; and (2) Waughs database. Furthermore, the mean of absolute error rate (MAER) and average error are used as evaluation criteria to compare the proposed method with other methods. It demonstrates that the proposed method is superior to existing methods for estimating null values in relational database systems.

Fuzzy clustering-based on aggregate attribute method

This paper, we propose a fuzzy clustering-based on aggregate attribute method for classification tasks, which comprises three phases: (1) Calculate the aggregate attribute values. (2) Apply fuzzy clustering to cluster the aggregate values. (3) Predict the testing data’s class. For verifying proposed method, we use two datasets to illustrate our performance, the datasets are: (1) Iris; (2) Wisconsin-breast-cancer dataset. Finally, we compare with other methods; it is shown that our proposed method is better than other methods.

Partitional approach for estimating null value in relational database

In this paper, we propose a partitional approach for estimating null value (1) Firstly, we utilize stepwise regression to select the important attributes from the database. (2) Secondly, we use a partitional approach to build the data category. The data partitioned by the first two important attributes. (3) Thirdly, we apply the clustering method to cluster output data. (4) Fourthly, Calculate the degree of influential between the attributes. There are two ways to calculate the degree of influential. One is correlation coefficient and the other is regression coefficients. (5) To verify our method, this paper utilizes a practical human resource database in Taiwan, and Mean of Absolute Error Rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves better than other methods for estimating null values in relational database systems.