Journal of Intelligence and Information Systems,
Vol. 1, No. 1, June 1995
Fuzzy Cognitive Map-Based Approach to Causal Knowledge Base Construction and Bi-Directional Inference Method -Applications to Stock Market Analysis-
Kun-Chang Lee, Seok Jin Joo, and Hyunsoo Kim
Vol. 1, No. 1, Page: 1 ~ 22
A Review of Artificial Intelligence Models in Business Classification
Ingoo Han, Young-sig Kwon, and Hong-kyu Jo
Vol. 1, No. 1, Page: 23 ~ 41
Business researchers have traditionally used statistical techniques for classification. In late 1980's, inductive learning started to be used for business classification. Recently, neural network began to be applied for business classification. This study reviews the business classification studies, identifies a neural network a, pp.oach as the most powerful classification tool, and discusses the problems and issues in neural network applications.
Inference Method for Rule-based Knowledge Representation with Fuzzy values and Certainty Factors
Kun Myung Lee, Choong Ho Jo, and Kwang Hyung Lee
Vol. 1, No. 1, Page: 43 ~ 59
Blackboard Scheduler Control Knowledge for Recursive Heuristic Classification
Young-Tack Park
Vol. 1, No. 1, Page: 61 ~ 72
Dynamic and explicit ordering of strategies is a key process in modeling knowledge-level problem-solving behavior. This paper addressed the important problem of howl to make the scheduler more knowledge-intensive in a way that facilitates the acquisition, integration, and maintenance of the scheduler control knowledge. The solution a, pp.oach described in this paper involved formulating the scheduler task as a heuristic classification problem, and then implementing it as a classification expert system. By doing this, the wide spectrum of known methods of acquiring, refining, and maintaining the knowledge of a classification expert system are a, pp.icable to the scheduler control knowledge. One important innovation of this research is that of recursive heuristic classification : this paper demonstrates that it is possible to formulate and solve a key subcomponent of heuristic classification as heuristic classification problem. Another key innovation is the creation of a method of dynamic heuristic classification : the classification alternatives that are selected among are dynamically generated in real-time and then evidence is gathered for and aginst these alternatives. In contrast, the normal model of heuristic classification is that of structured selection between a set of preenumerated fixed alternatives.
A Concurrent Engineering Approach to Expert System Development
Kwang Ho Park
Vol. 1, No. 1, Page: 73 ~ 89
Comparison of Alternative knowledge Acquisition Methods for Allergic Rhinitis
Young-Moon Chae, Seung-Kyu Chung, Jae-Gwon Suh, Seung-Hee Ho, and In-Yong Park
Vol. 1, No. 1, Page: 91 ~ 109
Keywords : allergic rhinitis, neural network, case-based reasoning, covariance structure modeling, discriminant analysis
This paper compared four knowledge acquisition methods (namely, neural network, case-based reasoning, discriminant analysis, and covariance structure modeling) for allergic rhinitis. The data were collected from 444 patients with suspected allergic rhinitis who visited the Otorlaryngology Deduring 1991-1993. Among four knowledge acquisition methods, the discriminant model had the best overall diagnostic capability (78%) and the neural network had slightly lower rate(76%). This may be explained by the fact that neural network is essentially non-linear discriminant model. The discriminant model was also most accurate in predicting allergic rhinitis (88%). On the other hand, the CSM had the lowest overall accuracy rate (44%) perhaps due to smaller input data set. However, it was most accuate in predicting non-allergic rhinitis (82%).
Constraint Directed Course Scheduling in Meta-Programming
Jong Jin Jung, and Geun Sik Jo
Vol. 1, No. 1, Page: 111 ~ 122
Usability Test of Non-Financial Information in Bankruptcy Prediction using Artificial Neural Network -The Case of Small and Medium-Sized Firms-
Jae Sik Lee, and Jae Hong Han
Vol. 1, No. 1, Page: 123 ~ 134
Time Series Analysis Using Neural Networks : Forecasting Performance Analysis with M1-Competition Data
Won Chul Ji
Vol. 1, No. 1, Page: 135 ~ 148
Neural Networks have been advocated as an alternative to statistical forecasting methods. However, the empirical evidences are not consistent. In the present experiments, multi-layered perceptron (MLP) are adopted as approximator to the time series generating processes. To prevent the MLP from being overfitted to the given time series, the information obtained from ARMA modeling is used to determine the architecture of MLP. The proposed approach was tested empirically using the subsamples of the 111 time series used in the first Markridakis Competition. The forecasting results were analyzed to find out the factors that affect the performance of MLP. The experimental results show that the proposed approach outperforms ARMA models in terms of fitting and forecasting accuracy. In addition, it is found that the use of deseasonalized data improves the forecasting accuracy of MLP.

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