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Open AccessJournal ArticleDOI

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.
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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.

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Citations
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Journal ArticleDOI

Classification in the Presence of Label Noise: A Survey

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

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.
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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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Book

The Nature of Statistical Learning Theory

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

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.
Book

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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