Instance-Based Learning Algorithms
Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Principles of data mining
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Machine Learning
A Noise Filtering Method for Inductive Concept Learning
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Analysis of evidence-theoretic decision rules for pattern classification
Pattern Recognition
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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The paper describes a novel approach to inductive learning based on a 'conflict estimation based learning' (CEL) algorithm. CEL is a new learning strategy, and unlike conventional methods CEL does not construct explicit abstractions of the target concept. Instead, CEL classifies unknown examples by adding them to each class of the training examples and measuring how much noise is generated. The class that results in the least noise, i.e., the class that least conflicts with the given example is chosen as the output. In this paper, we describe the underlying principles behind the CEL algorithm, a methodology for its construction, and then summarize convincing empirical evidence that suggests that CEL can be a perfect solution in real-world decision making applications.