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CONTEXT-BASED KNOWLEDGE SUPPORT FOR PROBLEM-SOLVING BY RULE-INFERENCE AND CASE-BASED REASONING
(2011-07-01)
Ke, Chih-Kun
;
Liu, Duen-Ren
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring relevant context of problem situations. Situation features are generally occurred according to the context characteristics of problems. Moreover, situation features collected are usually partial or incomplete. Workers need to use knowledge inferred from relevant context information and previous problem-solving experience to clarify the causes and take appropriate action effectively. In this paper, we propose to use rule inference to infer possible situation features based on context information. Association rule mining is used to discover context-based inference rules from historical problem-solving logs. The discovered patterns identify frequent associations between context information and situation features, and therefore, can be used to infer more situation features. By considering the inferred situation features, case-based reasoning can then be employed to identify similar situations effectively. Moreover, we employ information retrieval techniques to extract context-based situation profiles to model workers' information needs when handling problem situations in certain context. Effective knowledge support can thus be facilitated by providing workers with situation-relevant information based on the profiles. We develop a prototype system to demonstrate the effectiveness of providing context-based relevant information and decision-making knowledge to help workers solve problems.
Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis
(2012-04-01)
Huang, Mei-Ling
;
Hung, Yung-Hsiang
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Lee, Wen-Ming
;
Li, R. K.
;
Wang, Tzu-Hao
Breast cancer is a common to females world-wide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.
Development of an adaptive learning case recommendation approach for problem-based e-learning on mathematics teaching for students with mild disabilities
(2009-04-01)
Chu, Hui-Chuan
;
Chen, Tsung-Yi
;
Lin, Chia-Jou
;
Liao, Min-Ju
;
Chen, Yuh-Min
Most e-learning platforms offer theoretical knowledge content but not practical knowledge required for problem solving. This study proposed a problem-based e-learning (PBeL) model which incorporates the problem-based learning (PBL) theory, social constructivism, and situated learning theories to assist regular and special education teachers in effectively developing knowledge for mathematics teaching for students with mild disabilities. To support adaptive case-based learning in the proposed PBeL and to adequately address the real complexity and diversity of the learning problems of students' with mild disabilities, this research also developed an adaptive case recommendation approach which identifies the most suitable authentic learning cases based on the characteristics of learners (teachers), the strengths, weaknesses, and types of disabilities of their students, the teaching problems of various mathematical topics, and the teaching context in order to facilitate adaptive case-based learning in the context of problem-based e-learning for regular and special education teachers' knowledge development. Clustering and information retrieval techniques were used to construct the context and content maps for case-based reasoning with the capability of semantics identification. The adaptive recommendation approach not only enables the realization of adaptive PBeL, but also enhances teachers' practical knowledge and assists them to solve students' learning problems. (C) 2008 Elsevier Ltd. All rights reserved.
Zero anaphora resolution by case-based reasoning and pattern conceptualization
(2009-05-01)
Wu, Dian-Song
;
Liang, Tyne
;
資訊工程學系
;
Department of Computer Science
Effective anaphora resolution is helpful to many applications of natural language processing such as machine translation, summarization and question answering. In this paper, a novel resolution approach is proposed to tackle zero anaphora, which is the most frequent type of anaphora shown in Chinese texts. Unlike most of the previous approaches relying on hand-coded rules, our resolution is mainly constructed by employing case-based reasoning and pattern conceptualization. Moreover, the resolution is incorporated with the mechanisms to identify cataphora and non-antecedent instances so as to enhance the resolution performance. Compared to a general rule-based approach, the proposed approach indeed improves the resolution performance by achieves 78% recall and 79% precision on solving 1051 zero anaphora instances in 382 narrative texts. (C) 2008 Elsevier Ltd. All rights reserved.

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