Wei-Chou Chen

Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms

The mutation operation is critical to the success of genetic algorithms since it diversifies the search directions and avoids convergence to local optima. The earliest genetic algorithms use only one mutation operator in producing the next generation. Each problem, even each stage of the genetic process in a single problem, may require appropriately different mutation operators for best results. Determining which mutation operators should be used is quite difficult and is usually learned through experience or by trial-and-error. This paper proposes a new genetic algorithm, the dynamic mutation genetic algorithm, to resolve these difficulties. The dynamic mutation genetic algorithm simultaneously uses several mutation operators in producing the next generation. The mutation ratio of each operator changes according to evaluation results from the respective offspring it produces. Thus, the appropriate mutation operators can be expected to have increasingly greater effects on the genetic process. Experiments are reported that show the proposed algorithm performs better than most genetic algorithms with single mutation operators.

A novel manufacturing defect detection method using association rule mining techniques

In recent years, manufacturing processes have become more and more complex, and meeting high-yield target expectations and quickly identifying root-cause machinesets, the most likely sources of defective products, also become essential issues. In this paper, we first define the root-cause machineset identification problem of analyzing correlations between combinations of machines and the defective products. We then propose the Root-cause Machine Identifier (RMI) method using the technique of association rule mining to solve the problem efficiently and effectively. The experimental results of real datasets show that the actual root-cause machinesets are almost ranked in the top 10 by the proposed RMI method.

An efficient bit-based feature selection method

Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach.

A framework of decision support systems for use on the World Wide Web

Decision making is one of the most important activities for human beings. Complex decision making problems are full of uncertainty and ambiguities. A large amount of data is needed and several strategies must be applied at the same time in order to get a good decision. Research about decision support systems has been proposed to help people effectively solve these kinds of problems. Recently, World Wide Web applications have grown very rapidly and have made a significant impact on computer systems. In this paper, we propose a framework for Internet computing on decision support systems by modifying traditional decision support systems on the World Wide Web. Modules of the proposed framework were also designed to meet the requirements of both decision support and Internet computing. An actual implementation of a travelling schedule domain was conducted to show the feasibility of the proposed framework. The proposed framework provides a feasible solution to building World Wide Web decision support systems in real applications.

A parallelized indexing method for large-scale case-based reasoning

Abstract Case-based reasoning (CBR) is a problem-solving methodology commonly seen in artificial intelligence. It can correctly take advantage of the situations and methods in former cases to find out suitable solutions for new problems. CBR must accurately retrieve similar prior cases for getting a good performance. In the past, many researchers proposed useful technologies to handle this problem. However, the performance of retrieving similar cases may be greatly influenced by the number of cases. In this paper, the performance issue of large-scale CBR is discussed and a parallelized indexing architecture is then proposed for efficiently retrieving similar cases in large-scale CBR. Several algorithms for implementing the proposed architecture are also described. Some experiments are made and the results show the efficiency of the proposed method.

A framework of features selection for the case-based reasoning

CBR is a problem solving technique that reuses past cases and experiences to find a solution to problems. A critical issue in case based reasoning is to select the correct and enough features to represent a case. For this reason, the analysis of cases and extraction of the necessary features to represent a case are highly recommended in building a CBR system. However, this task is difficult to carry out since such knowledge often cannot be successfully and exhaustively captured and represented. A framework of feature mining system for the case based reasoning including two phases is proposed. The techniques of feature selection, data analysis and machine learning can thus be effectively integrated. This will promote flexibility and expandability of the case based reasoning system.

A novel manufacturing defect detection method using data mining approach

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

A Similarity Indexing Method for the Data Warehousing - Bit-Wise Indexing Method

Data warehouse is an information provider that collects necessary data from individual source databases to support the analytical processing of decision-support functions. Recently, research about the indexing technologies of data warehousing has been proposed to help efficient on-line analytical processing (OLAP). In the past decades, some novel indexing technologies of data warehousing were proposed to retrieve the information precisely. However, the concept of similarity indexing technology in the increasingly larger data warehousing was seldom been discussed. In this paper, the performance issue of approximation indexing technology in the data warehousing is discussed and a new similarity indexing method, called bit-wise indexing method, and the corresponding efficient algorithms are proposed for retrieving the similar cases of a case-based reasoning system using a data warehouse to be the storage space. Some experiments are made for comparing the performance with two other methods and the results show the efficiency of the proposed method.

Using the compressed data model in object-oriented data warehousing

A data warehouse is an information provider that collects necessary data from individual source databases to support the analytical processing of decision-support functions. In the past, research on data warehouses primarily focused on relational data models. In this paper, the concept of object-oriented data warehousing is introduced and discussed. A new data model, called the compressed data model, is proposed for storing the data in the object-oriented data warehouse. The data model will form new classes according to the definitions of views, such that the query performance and security can be improved. Three incremental maintenance algorithms, including instance insertion, deletion and update, are proposed to maintain the consistency between the data warehouse and the source databases.

Internet computing on decision support systems

Decision-making is one of the most important activities for human beings. Complex decision-making problems are full of uncertainties and ambiguities. Research about decision support systems has been proposed to help people effectively solve this kind of problems. In this paper, we attempt to propose a framework of Internet computing on decision support systems by modifying traditional decision support systems on World Wide Web. Ten modules of the proposed framework, including request, request converter, task dispatcher, database management systems, databases, model base management system, model bases, presentation converter, presentation, and security are designed to meet the requirement of Internet computing. A prototype implementation was conducted to show the feasibility of the proposed method.