• Title/Summary/Keyword: Rule Extraction

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Extraction of Expert Knowledge Based on Hybrid Data Mining Mechanism (하이브리드 데이터마이닝 메커니즘에 기반한 전문가 지식 추출)

  • Kim, Jin-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.6
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    • pp.764-770
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    • 2004
  • This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

Rule Extraction from Neural Networks : Enhancing the Explanation Capability

  • Park, Sang-Chan;Lam, Monica-S.;Gupta, Amit
    • Journal of Intelligence and Information Systems
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    • v.1 no.2
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    • pp.57-71
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    • 1995
  • This paper presents a rule extraction algorithm RE to acquire explicit rules from trained neural networks. The validity of extracted rules has been confirmed using 6 different data sets. Based on experimental results, we conclude that extracted rules from RE predict more accurately and robustly than neural networks themselves and rules obtained from an inductive learning algorithm do. Rule extraction algorithm for neural networks are important for incorporating knowledge obtained from trained networks into knowledge based systems. In lieu of this, the proposed RE algorithm contributes to the trend toward developing hybrid and versatile knowledge-based system including expert systems and knowledge-based decision su, pp.rt systems.

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Neural network rule extraction for credit scoring

  • Bart Baesens;Rudy Setiono;Lille, Valerina-De;Stijn Viaene
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2001.01a
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    • pp.128-132
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    • 2001
  • In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried our on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed The rule extraction algorithms, Neurolonear, Neurorule. Trepan and Nefclass, have different characteristics, with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree(rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional if -then rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.

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Development of Automatic Rule Extraction Method in Data Mining : An Approach based on Hierarchical Clustering Algorithm and Rough Set Theory (데이터마이닝의 자동 데이터 규칙 추출 방법론 개발 : 계층적 클러스터링 알고리듬과 러프 셋 이론을 중심으로)

  • Oh, Seung-Joon;Park, Chan-Woong
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.6
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    • pp.135-142
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    • 2009
  • Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

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Fault Detection, Diagnosis, and Optimization of Wafer Manufacturing Processes utilizing Knowledge Creation

  • Bae Hyeon;Kim Sung-Shin;Woo Kwang-Bang;May Gary S.;Lee Duk-Kwon
    • International Journal of Control, Automation, and Systems
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    • v.4 no.3
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    • pp.372-381
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    • 2006
  • The purpose of this study was to develop a process management system to manage ingot fabrication and improve ingot quality. The ingot is the first manufactured material of wafers. Trace parameters were collected on-line but measurement parameters were measured by sampling inspection. The quality parameters were applied to evaluate the quality. Therefore, preprocessing was necessary to extract useful information from the quality data. First, statistical methods were used for data generation. Then, modeling was performed, using the generated data, to improve the performance of the models. The function of the models is to predict the quality corresponding to control parameters. Secondly, rule extraction was performed to find the relation between the production quality and control conditions. The extracted rules can give important information concerning how to handle the process correctly. The dynamic polynomial neural network (DPNN) and decision tree were applied for data modeling and rule extraction, respectively, from the ingot fabrication data.

A GA-based Rule Extraction for Bankruptcy Prediction Modeling (유전자 알고리즘을 활용한 부실예측모형의 구축)

  • Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.7 no.2
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    • pp.83-93
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    • 2001
  • Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

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Fuzzy rule Extraction of Neuro-Fuzzy System using EM algorithm (EM 알고리즘에 의한 뉴로-퍼지 시스템의 퍼지 규칙 생성)

  • 김승석;곽근창;유정웅;전명근
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2002.05a
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    • pp.170-173
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    • 2002
  • 본 논문에서는 여러 분야에서 널리 응용되고 있는 적응 뉴로-퍼지 시스템(ANFIS)에서의 효과적인 퍼지 규칙 생성방법을 제안한다. ANFIS의 성능 개선을 위해 구조동정을 수행함에 있어서 전제부 파라미터는 EM(Expectation-Maximization) 알고리즘을 적용하였으며, 파라미터학습은 Jang에 의한 하이브리드 방법을 적용한다. 여기서 초기의 중심과 분산을 구하기 위해 FCM(Fuzzy c-means) 클러스터링 기법을 사용하였다. 이렇게 함으로서 적은 규칙 수를 가지면서도 효율적인 퍼지 규칙을 얻을 수 있도록 하였다. 이들 방법의 유용함을 보이고자 Box-Jenkins의 가스로 데이터에 적용하여 제안된 방법이 이전의 연구보다 좋은 결과를 보임을 보이고자 한다

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Prediction of User Preferred Cosmetic Brand Based on Unified Fuzzy Rule Inference

  • Kim, Jin-Sung
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2005.11a
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    • pp.271-275
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    • 2005
  • In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this Purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between $0\∼1$. Second, RDB and SQL(Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS(Knowledge Management Systems)

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A Study on the Self-Evolving Expert System using Neural Network and Fuzzy Rule Extraction (인공신경망과 퍼지규칙 추출을 이용한 상황적응적 전문가시스템 구축에 관한 연구)

  • 이건창;김진성
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.3
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    • pp.231-240
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    • 2001
  • Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

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Prediction of User's Preference by using Fuzzy Rule & RDB Inference: A Cosmetic Brand Selection

  • Kim, Jin-Sung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.4
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    • pp.353-359
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    • 2005
  • In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).