Class confidence weighted kNN algorithms for imbalanced data sets

Wei Liu, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

108 Scopus citations

Abstract

In this paper, a novel k-nearest neighbors (kNN) weighting strategy is proposed for handling the problem of class imbalance. When dealing with highly imbalanced data, a salient drawback of existing kNN algorithms is that the class with more frequent samples tends to dominate the neighborhood of a test instance in spite of distance measurements, which leads to suboptimal classification performance on the minority class. To solve this problem, we propose CCW (class confidence weights) that uses the probability of attribute values given class labels to weight prototypes in kNN. The main advantage of CCW is that it is able to correct the inherent bias to majority class in existing kNN algorithms on any distance measurement. Theoretical analysis and comprehensive experiments confirm our claims.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 15th Pacific-Asia Conference, PAKDD 2011, Proceedings
PublisherSpringer Verlag
Pages345-356
Number of pages12
EditionPART 2
ISBN (Print)9783642208461
DOIs
StatePublished - 2011
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6635 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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