Ta-Cheng Chen

Bio: Ta-Cheng Chen is an academic researcher from National Formosa University. The author has contributed to research in topics: Elevator & Evolutionary computation. The author has an hindex of 13, co-authored 42 publications receiving 788 citations. Previous affiliations of Ta-Cheng Chen include Asia University (Taiwan).

A GAs based approach for mining breast cancer pattern

Abstract: Data mining usually means the methodologies and tools for the efficient new knowledge discovery from databases. In this paper, a genetic algorithms (GAs) based approach to assess breast cancer pattern is proposed for extracting the decision rules including the predictors, the corresponding inequality and threshold values simultaneously so as to building a decision-making model with maximum prediction accuracy. Early many studies of handling the breast cancer diagnostic problems used the statistical related techniques. As the diagnosis of breast cancer is highly nonlinear in nature, it is hard to develop a comprehensive model taking into account all the independent variables using conventional statistical approaches. Recently, numerous studies have demonstrated that neural networks (NNs) are more reliable than the traditional statistical approaches and the dynamic stress method. The usefulness of using NNs have been reported in literatures but the most obstacle is the in the building and using the model in which the classification rules are hard to be realized. We compared our results against a commercial data mining software, and we show experimentally that the proposed rule extraction approach is promising for improving prediction accuracy and enhancing the modeling simplicity. In particular, our approach is capable of extracting rules which can be developed as a computer model for prediction or classification of breast cancer potential like expert systems.

A Survey on Evolutionary Computation Approaches to Feature Selection

Abstract: Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

Subset Selection in Regression

Abstract: 8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Recent Advances in Optimal Reliability Allocation

Abstract: Reliability has become a greater concern in recent years, because high-tech industrial processes with ever increasing levels of sophistication comprise most engineering systems today. To keep pace with this rapidly developing field, this paper provides a broad overview of recent research on reliability optimization problems and their solution methodologies. In particular, we address issues related to: 1) universal generating-function-based optimal multistate system design; 2) percentile life employed as a system performance measure; 3) multiobjective optimization of reliability systems, especially with uncertain component-reliability estimations; and 4) innovation and improvement in traditional reliability optimization problems, such as fault-tolerance mechanism and cold-standby redundancy-involved system design. New developments in optimization techniques are also emphasized in this paper, especially the methods of ant colony optimization and hybrid optimization. We believe that the interesting problems that are reviewed here are deserving of more attention in the literature. To that end, this paper concludes with a discussion of future challenges related to reliability optimization

A comprehensive review of tolerancing research

Abstract: Ever since the plus/minus limits on dimensions first started to appear on engineering drawings in the early 1900s, tolerances have been one of the most important issues for every engineer involved in the product realization processes. In particular, with the advancement of computers and CAD/CAM techniques in the 1970s, the tolerance-related issues have continuously drawn the attention of many researchers since then. As a result, a tremendous number of research articles have been published over the last 30 years. This paper aims at a comprehensive state-of-the-art review on various tolerancing issues in design and manufacturing. However, due to the overwhelming number of existing research publications, any reviews on tolerancing issues could by no means be exhaustive. Rather, this review attempts to provide the reader with a view toward a balanced understanding of the various problems in tolerancing by presenting some typical research work for each of the classified fields, and tries to draw the potential ...