Sheng-Tun Li

Bio: Sheng-Tun Li is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Fuzzy logic & Probabilistic forecasting. The author has an hindex of 26, co-authored 119 publications receiving 2034 citations. Previous affiliations of Sheng-Tun Li include Tamkang University & National Kaohsiung First University of Science and Technology.

The evaluation of consumer loans using support vector machines

Abstract: The commencement of the Basel II requirement, popularization of consumer loans and the intense competition in financial market has increased the awareness of the critical delinquency issue for financial institutions in granting loans to potential applicants. In the past few decades, the scheme of artificial neural networks has been successfully applied to the financial field. Recently, the Support Vector Machine (SVM) has emerged as the better neural network in dealing with classification and forecasting problems due to its superior features of generalization performance and global optimum. This study develops a loan evaluation model using SVM to identify potential applicants for consumer loans. In addition to conducting experiments on performance comparison via cross-validation and paired t test, we analyze misclassification errors in terms of Type I and Type II and their effect on selecting network parameters of SVM. The analysis findings facilitate the development of a useful visual decision-support tool. The experimental results using a real-world data set reveal that SVM surpasses traditional neural network models in generalization performance and visualization via the visual tool, which helps decision makers determine appropriate loan evaluation strategies.

A FCM-based deterministic forecasting model for fuzzy time series

Abstract: The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness inherent in the data collected. A variety of forecasting models including high-order models have been devoted to improving forecasting accuracy. However, the high-order forecasting approach is accompanied by the crucial problem of determining an appropriate order number. Consequently, such a deficiency was recently solved by Li and Cheng [S.-T. Li, Y.-C. Cheng, Deterministic Fuzzy time series model for forecasting enrollments, Computers and Mathematics with Applications 53 (2007) 1904-1920] using a deterministic forecasting method. In this paper, we propose a novel forecasting model to enhance forecasting functionality and allow processing of two-factor forecasting problems. In addition, this model applies fuzzy c-means (FCM) clustering to deal with interval partitioning, which takes the nature of data points into account and produces unequal-sized intervals. Furthermore, in order to cope with the randomness of initially assigned membership degrees of FCM clustering, Monte Carlo simulations are used to justify the reliability of the proposed model. The superior accuracy of the proposed model is demonstrated by experiments comparing it to other existing models using real-world empirical data.

Data mining to aid policy making in air pollution management

Abstract: In the past two decades, the heavy environmental loading has led to the deterioration of air quality in Taiwan. The task of controlling and improving air quality has attracted a great deal of national attention. The Taiwanese government has since set up the National Air Quality Monitoring Network (TAQMN) to monitor nationwide air quality and adopted an array of measures to combat this problem. This study applies data mining to uncover the hidden knowledge of air pollution distribution in the voluminous data retrieved from monitoring stations in TAQMN. The mining process consists of data acquisition from Web sites of 71 data gathering stations nationwide, data pre-processing using multi-scale wavelet transforms, data pattern identification using cluster analysis, and final analysis in mapping the identified clusters to geographical locations. The application of multi-scale wavelet transforms contributes greatly in removing noises and identifying the trend of data. In addition, the proposed two-level self-organization map neural network demonstrates its ability in identifying clusters on the high-dimensional wavelet-transformed space. The identified distribution of suspended particulate PM10 represents a complete, national picture of the present air quality situation, which contrasts the present pollution districts, and could serve as an important reference for government agencies in evaluating present and devising future air pollution policies.

Deterministic fuzzy time series model for forecasting enrollments

Abstract: The fuzzy time series has recently received increasing attention because of its capability of dealing with vague and incomplete data. There have been a variety of models developed to either improve forecasting accuracy or reduce computation overhead. However, the issues of controlling uncertainty in forecasting, effectively partitioning intervals, and consistently achieving forecasting accuracy with different interval lengths have been rarely investigated. This paper proposes a novel deterministic forecasting model to manage these crucial issues. In addition, an important parameter, the maximum length of subsequence in a fuzzy time series resulting in a certain state, is deterministically quantified. Experimental results using the University of Alabama's enrollment data demonstrate that the proposed forecasting model outperforms the existing models in terms of accuracy, robustness, and reliability. Moreover, the forecasting model adheres to the consistency principle that a shorter interval length leads to more accurate results.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.