Ensemble confidence estimates posterior probability

Michael Muhlbaier, Apostolos Topalis, Robi Polikar

Research output: Contribution to journalConference articlepeer-review

24 Scopus citations

Abstract

We have previously introduced the Learn++ algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribution we show that the algorithm can also be used to estimate the posterior probability, or the confidence of its decision on each test instance. On three increasingly difficult tests that are specifically designed to compare posterior probability estimates of the algorithm to that of the optimal Bayes classifier, we have observed that estimated posterior probability approaches to that of the Bayes classifier as the number of classifiers in the ensemble increase. This satisfying and intuitively expected outcome shows that ensemble systems can also be used to estimate confidence of their output.

Original languageEnglish (US)
Pages (from-to)326-335
Number of pages10
JournalLecture Notes in Computer Science
Volume3541
DOIs
StatePublished - 2005
Externally publishedYes
Event6th International Workshop on Multiple Classifier Systems, MCS 2005 - Seaside, CA., United States
Duration: Jun 13 2005Jun 15 2005

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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