{"type":"ScholarlyArticle","title":"An Average-Case Analysis of the k-Nearest Neighbar Classifier for Noisy Domains","authors":[{"name":"Seishi Okamoto","slug":"seishi-okamoto","given":"Seishi","family":"Okamoto","affiliation":""},{"name":"Nobuhiro Yugami","slug":"nobuhiro-yugami","given":"Nobuhiro","family":"Yugami","affiliation":""}],"abstract":"This paper presents an average-case analysis of the k-nearest neighbor classifier(k-NN). Our analysis deals with m-of-n/l concepts, and handles three types of noise: relevant attribute noise, irrelevant attribute noise, and class noise. We formally compute the expected classification accuracy of k-NN after a certain fixed number of training instances. This accuracy is represented as a function of the domain characteristics. Then, the predicted behavior of k-NN for each type of noise is explored by using the accuracy function. We examine the classification accuracy of k-NN at various noise levels, and show how noise affects the accuracy of kNN. We also show the relationship between the optimal value of k and the number of training instances in noisy domains. Our analysis is supported with Monte Carlo simulations.","doi":null,"url":"https://ijcai.org/proceedings/1997/36","pdf":"https://ijcai.org/proceedings/1997/0036.pdf","markdown":"https://ijcai.org/proceedings/1997/36.md","bibtex":"https://ijcai.org/proceedings/1997/bibtex/36","volume":{"slug":"1997","title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1997","url":"https://ijcai.org/proceedings/1997/","year":1997,"location":"Nagoya, Japan","dates":"","editors":"Martha E. Pollack","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Case-based Reasoning","track":"Case-based Reasoning","number":36,"pages":{"first":238,"last":244},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}