Communications of the ACM
Communications of the ACM - Special issue on parallelism
Computational limitations on learning from examples
Journal of the ACM (JACM)
Instance-Based Learning Algorithms
Machine Learning
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Average case analysis of learning k-CNF concepts
ML92 Proceedings of the ninth international workshop on Machine learning
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
Artificial Intelligence Review - Special issue on lazy learning
Best-Case Results for Nearest-Neighbor Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Average-Case Analyses of the Nearest Neighbor Algorithm
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Average-Case Analysis of k-Nearest Neighbor Classifier
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Tractable Average-Case Analysis of Naive Bayesian Classifiers
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An average-case analysis of the k-nearest neighbor classifier for noisy domains
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
A complete and tight average-case analysis of learning monomials
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
A New Performance Evaluation Method for Two-Class Imbalanced Problems
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
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This paper presents average-case analyses of instance-based learning algorithms. The algorithms analyzed employ a variant of k-nearest neighbor classifier (k-NN). Our analysis deals with a monotone m-of-n target concept with irrelevant attributes, and handles three types of noise: relevant attribute noise, irrelevant attribute noise, and class noise. We formally represent the expected classification accuracy of k-NN as a function of domain characteristics including the number of training instances, the number of relevant and irrelevant attributes, the threshold number in the target concept, the probability of each attribute, the noise rate for each type of noise, and k. We also explore the behavioral implications of the analyses by presenting the effects of domain characteristics on the expected accuracy of k-NN and on the optimal value of k for artificial domains.