Mining competent case bases for case-based reasoning
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TLDR
This paper develops a theoretical framework for the error bound in case-based reasoning, and proposes a novel case-base mining algorithm guided by the theoretical results that returns a high-quality case base from raw data efficiently.About:
This article is published in Artificial Intelligence.The article was published on 2007-11-01 and is currently open access. It has received 60 citations till now. The article focuses on the topics: Case-based reasoning.read more
Citations
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Classification in the Presence of Label Noise: A Survey
Benoît Frénay,Michel Verleysen +1 more
TL;DR: In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.
Journal ArticleDOI
Ranking-order case-based reasoning for financial distress prediction
TL;DR: Empirical results indicate that ROCBR outperforms ECBR, MCBR, ICBR, MDA, and Logit significantly in financial distress prediction of Chinese listed companies 1 year prior to distress, if irrelevant information among features has been handled effectively.
Journal ArticleDOI
Concept drift detection via competence models
Ning Lu,Guangquan Zhang,Jie Lu +2 more
TL;DR: A competence-based concept detection method that requires no prior knowledge of case distribution and provides statistical guarantees on the reliability of the changes detected, as well as meaningful descriptions and quantification of these changes.
Journal ArticleDOI
Gaussian case-based reasoning for business failure prediction with empirical data in China
TL;DR: This study presents a hybrid Gaussian CBR (GCBR) system and indicates that GCBR produces superior performance in short-term BFP of Chinese listed companies in terms of both predictive accuracy and coefficient of variation.
Journal ArticleDOI
Predicting business failure using multiple case-based reasoning combined with support vector machine
TL;DR: Empirical results have indicated that Multi-CBR-SVM is feasible and validated for listed companies' business failure prediction in China.
References
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TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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