Estimation of false negatives in classification

Sandeep Mane, Jaideep Srivastava, San Yih Hwang, Jamshid Vayghan

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

3 Scopus citations

Abstract

In many classification problems such as spam detection and network intrusion, a large number of unlabeled test instances are predicted negative by the classifier. However, the high costs as well as time constraints on an expert's time prevent further analysis of the "predicted false" class instances in order to segregate the false negatives from the true negatives. A systematic method is thus required to obtain an estimate of the number of false negatives. A capture-recapture based method can be used to obtain an ML-estimate of false negatives when two or more independent classifiers are available. In the case for which independence does not hold, we can apply log-linear models to obtain an estimate of false negatives. However, as shown in this paper, lesser the dependencies among the classifiers, better is the estimate obtained for false negatives. Thus, ideally independent classifiers should be used to estimate the false negatives in an unlabeled dataset. Experimental results on the spam dataset from the UCI Machine Learning Repository are presented.

Original languageEnglish (US)
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
EditorsR. Rastogi, K. Morik, M. Bramer, X. Wu
Pages475-478
Number of pages4
DOIs
StatePublished - Dec 1 2004
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: Nov 1 2004Nov 4 2004

Publication series

NameProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Other

OtherProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
CountryUnited Kingdom
CityBrighton
Period11/1/0411/4/04

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