Authoritative citation KNN learning with noisy training datasets

Joseph Bernadt, Leen-Kiat Soh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we investigate the effectiveness of Citation K-Nearest Neighbors (KNN) learning with noisy training datasets. We devise an authority measure associated with each training instance that changes based on the outcome of Citation KNN classification. We show that by modifying only the authority measures, the classification accuracy by Citation KNN improves significantly in a variety of datasets with different noise levels. Also, by analyzing the authority measures, we are able to identify and correct noisy training instances.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04)
EditorsH.R. Arabnia, M. Youngsong
Pages916-921
Number of pages6
StatePublished - Dec 1 2004
EventProceedings of the International Conference on Artificial Intelligence, IC-AI'04 - Las Vegas, NV, United States
Duration: Jun 21 2004Jun 24 2004

Publication series

NameProceedings of the International Conference on Artificial Intelligence, IC-AI'04
Volume2

Conference

ConferenceProceedings of the International Conference on Artificial Intelligence, IC-AI'04
CountryUnited States
CityLas Vegas, NV
Period6/21/046/24/04

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Bernadt, J., & Soh, L-K. (2004). Authoritative citation KNN learning with noisy training datasets. In H. R. Arabnia, & M. Youngsong (Eds.), Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04) (pp. 916-921). (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04; Vol. 2).

Authoritative citation KNN learning with noisy training datasets. / Bernadt, Joseph; Soh, Leen-Kiat.

Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04). ed. / H.R. Arabnia; M. Youngsong. 2004. p. 916-921 (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bernadt, J & Soh, L-K 2004, Authoritative citation KNN learning with noisy training datasets. in HR Arabnia & M Youngsong (eds), Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04). Proceedings of the International Conference on Artificial Intelligence, IC-AI'04, vol. 2, pp. 916-921, Proceedings of the International Conference on Artificial Intelligence, IC-AI'04, Las Vegas, NV, United States, 6/21/04.
Bernadt J, Soh L-K. Authoritative citation KNN learning with noisy training datasets. In Arabnia HR, Youngsong M, editors, Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04). 2004. p. 916-921. (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04).
Bernadt, Joseph ; Soh, Leen-Kiat. / Authoritative citation KNN learning with noisy training datasets. Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04). editor / H.R. Arabnia ; M. Youngsong. 2004. pp. 916-921 (Proceedings of the International Conference on Artificial Intelligence, IC-AI'04).
@inproceedings{11b3c852566844fdbabf02696a360c60,
title = "Authoritative citation KNN learning with noisy training datasets",
abstract = "In this paper, we investigate the effectiveness of Citation K-Nearest Neighbors (KNN) learning with noisy training datasets. We devise an authority measure associated with each training instance that changes based on the outcome of Citation KNN classification. We show that by modifying only the authority measures, the classification accuracy by Citation KNN improves significantly in a variety of datasets with different noise levels. Also, by analyzing the authority measures, we are able to identify and correct noisy training instances.",
author = "Joseph Bernadt and Leen-Kiat Soh",
year = "2004",
month = "12",
day = "1",
language = "English (US)",
isbn = "1932415335",
series = "Proceedings of the International Conference on Artificial Intelligence, IC-AI'04",
pages = "916--921",
editor = "H.R. Arabnia and M. Youngsong",
booktitle = "Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04)",

}

TY - GEN

T1 - Authoritative citation KNN learning with noisy training datasets

AU - Bernadt, Joseph

AU - Soh, Leen-Kiat

PY - 2004/12/1

Y1 - 2004/12/1

N2 - In this paper, we investigate the effectiveness of Citation K-Nearest Neighbors (KNN) learning with noisy training datasets. We devise an authority measure associated with each training instance that changes based on the outcome of Citation KNN classification. We show that by modifying only the authority measures, the classification accuracy by Citation KNN improves significantly in a variety of datasets with different noise levels. Also, by analyzing the authority measures, we are able to identify and correct noisy training instances.

AB - In this paper, we investigate the effectiveness of Citation K-Nearest Neighbors (KNN) learning with noisy training datasets. We devise an authority measure associated with each training instance that changes based on the outcome of Citation KNN classification. We show that by modifying only the authority measures, the classification accuracy by Citation KNN improves significantly in a variety of datasets with different noise levels. Also, by analyzing the authority measures, we are able to identify and correct noisy training instances.

UR - http://www.scopus.com/inward/record.url?scp=12744260611&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=12744260611&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:12744260611

SN - 1932415335

SN - 9781932415339

T3 - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04

SP - 916

EP - 921

BT - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04)

A2 - Arabnia, H.R.

A2 - Youngsong, M.

ER -