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A Study on Forecasting Accuracy Improvement of Case Based Reasoning Approach Using Fuzzy Relation
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In-Ho Lee (Dept. of the Accounting Information, Woongji Accounting & Tax College)
Kyung-Shik Shin (College of Business Admin., Ewha Womans University)
Vol. 16, No. 4, Page: 67 ~ 84
Keywords
Forecasting, Symbolic Data, Fuzzy Relation, Case-Based Reasoning, Similarity Matrix
Abstract
In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.
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퍼지 관계를 활용한 사례기반추론 예측 정확성 향상에 관한 연구
이인호 (웅지세무대학 회계정보과)
신경식 (이화여자대학교 경영대학 경영학과)
Abstract
미래에 대한 정확한 예측은 경영자, 또는 기업이 수행하는 경영의사결정에 매우 중요한 역할을 한다. 예측만 정확하다면 경영의사결정의 질은 매우 높아질 수 있을 것이다. 하지만 점점 가속화되고 있는 경영 환경의 변화로 말미암아 미래 예측을 정확하게 하는 일은 점점 더 어려워지고 있다. 이에 기업에서는 정확한 예측을 위하여 전문가의 휴리스틱뿐만 아니라 과학적 예측모형을 함께 활용하여 예측의 성과를 높이는 노력을 해 오고 있다. 본 연구는 사례기반추론모형을 예측을 위한 기본 모형으로 설정하고, 데이터 간의 유사도 측정에 퍼지 관계의 개념을 적용함으로써 개선된 예측성과를 얻고자 하였다. 특히, 독립변수 중 기호 데이터 형식의 속성을 가지는 변수들간의 유사도를 측정하기 위해 이진논리의 개념(일치여부의 판단)과 퍼지 관계 및 합성의 개념을 이용하여 도출된 유사도 매트릭스를 사용하였다. 연구 결과, 기호 데이터 형식의 속성을 가지는 변수들 간의 유사도 측정에서 퍼지 관계 및 합성의 개념을 적용하는 방법이 이진논리의 개념을 적용하는 방법과 비교하여 더 우수한 예측정확성을 나타내었다. 그러나 유사도 측정을 위해 다양한 퍼지합성방법(Max-min 합성, Max-product 합성, Max-average 합성)을 적용하여 예측하는 경우에는 예측정확성 측면에서 퍼지 합성방법 간의 통계적인 차이는 유의하지 않았다. 본 연구는 사례기반추론 모형의 구축에서 가장 중요한 유사도 측정에 있어서 퍼지 관계 및 퍼지 합성의 개념을 적용함으로써 유사도 측정 및 적용 방법론을 제시하였다는데 의의가 있다.
Cite this article
JIIS Style
Lee, I.-H., and K.-S. Shin, "A Study on Forecasting Accuracy Improvement of Case Based Reasoning Approach Using Fuzzy Relation ", Journal of Intelligence and Information Systems, Vol. 16, No. 4 (2010), 67~84.

IEEE Style
In-Ho Lee, and Kyung-Shik Shin, "A Study on Forecasting Accuracy Improvement of Case Based Reasoning Approach Using Fuzzy Relation ", Journal of Intelligence and Information Systems, vol. 16, no. 4, pp. 67~84, 2010.

ACM Style
Lee, I.-H., and Shin, K.-S., 2010. A Study on Forecasting Accuracy Improvement of Case Based Reasoning Approach Using Fuzzy Relation . Journal of Intelligence and Information Systems. 16, 4, 67--84.
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@article{Lee:JIIS:2010:420,
author = {Lee, In-Ho and Shin, Kyung-Shik},
title = {A Study on Forecasting Accuracy Improvement of Case Based Reasoning Approach Using Fuzzy Relation },
journal = {Journal of Intelligence and Information Systems},
issue_date = {December 2010},
volume = {16},
number = {4},
month = Dec,
year = {2010},
issn = {2288-4866},
pages = {67--84},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Forecasting, Symbolic Data, Fuzzy Relation, Case-Based Reasoning and Similarity Matrix },
}
%0 Journal Article
%1 420
%A In-Ho Lee
%A Kyung-Shik Shin
%T A Study on Forecasting Accuracy Improvement of Case Based Reasoning Approach Using Fuzzy Relation
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 16
%N 4
%P 67-84
%D 2010
%R
%I Korea Intelligent Information System Society