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Journal of Intelligence and Information Systems,
Vol. 4, No. 2, December 1998
Classification Performance Comparison of Inductive Learning Methods : The Case of Corporate Credit Rating
Sang-Ho Lee, and Won Chul Jhee
Vol. 4, No. 2, Page: 1 ~ 21
Abstract
Enhancement of Forecasting Accuracy in Time-Series Data, Basedon Wavelet Transformation and Neural Network Training
Seung-Won Shin, Jong-Uk Choi, and Jeong-Hyun Rho
Vol. 4, No. 2, Page: 23 ~ 34
Abstract
Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.
A Design of Expert System for Reconstruction of Automobile Collision Accidents
Hyun-Kyung Kim
Vol. 4, No. 2, Page: 35 ~ 44
Abstract
Knowledge-Based vs. Constraints-Based Scheduling : A Case Study of Gate Allocation Problem
Jong-Yoon Yang, and Geun-Sik Jo
Vol. 4, No. 2, Page: 45 ~ 59
Keywords : Constraints Satisfaction Problems, Knowledge-Based System, Rule-Based System
Abstract
Unsupervised Document Clustering for Constructing User Profile of Web Agent
Jae-jun Oh, and Young-Tack Park
Vol. 4, No. 2, Page: 61 ~ 83
Abstract
Data Modeling Methods for Performance Enhancement
Su-Yeon Kim, Sang-Ho Lee, and Eui-Ho Suh
Vol. 4, No. 2, Page: 85 ~ 102
Abstract
Comparison and Experiments on the Electronic Commerce Agent Protocols under Time-Bounded Negotiation Framework
Kyoung-Jun Lee, and Yong-Sik Chang
Vol. 4, No. 2, Page: 103 ~ 116
Abstract
An Architecture for the Expert System for the Telecommunications Internetworking Design
Dai Yon Cho
Vol. 4, No. 2, Page: 117 ~ 128
Keywords : Case-Based System, Expert System, Internetworking, Telecommunications
Abstract
CBR is a knowledge-based system that utilizes the previous knowledge or experience to solve the current problem. In previous CBR research, the emphases are mainly put on the development of more sophisticated indexing mechanism for past cases or the most similar case retrieving methodology out of a group of previous cases. In this paper, discussed is a CBR system that is able to take advantage of the case or knowledge that does not belong to the past in the telecommunications internetworking design area. And the architecture for such CBR system is proposed. Finally, the performance of the CBR system is shown through an ablation experiment
Development of Data Mining Tool Using S-PLUS and StatServer
In-seok Jeong, and Jae-June Lee
Vol. 4, No. 2, Page: 129 ~ 139
Keywords : data mining, S-PLUS, StatServer, tree
Abstract
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