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Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method

Published: 28 November 2018 Publication History

Abstract

We proposed a novel method that selects an optimal classifier model's parameter status through the uncertainty measure evaluation of the estimated class boundaries instead of an estimation of the classification error probability. A key feature of our method is its potential to perform a classifier parameter status selection without a separate validation sample set that can be easily applied to any reasonable type of classifier model, unlike traditional approaches that often need a validation sample set or are sometimes less practical. In this paper, we first summarize our method and its experimental evaluation results and introduce the mathematical formalization for the posterior probability estimation procedure adopted in it. Then we show the convergence property of the estimation procedure and finally demonstrate our method's optimality in a practical situation where only a finite number of training samples are available.

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Cited By

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  • (2021)An Improved Boundary Uncertainty-Based Estimation for Classifier EvaluationJournal of Signal Processing Systems10.1007/s11265-021-01671-1Online publication date: 10-Jun-2021
  • (2019)Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron ClassifiersIntegrated Uncertainty in Knowledge Modelling and Decision Making10.1007/978-3-030-14815-7_25(295-307)Online publication date: 7-Mar-2019

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  1. Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method

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    cover image ACM Other conferences
    SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning
    November 2018
    177 pages
    ISBN:9781450366052
    DOI:10.1145/3297067
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 28 November 2018

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    Author Tags

    1. Pattern recognition
    2. class boundary uncertainty
    3. classification
    4. classifier model selection

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    View all
    • (2021)An Improved Boundary Uncertainty-Based Estimation for Classifier EvaluationJournal of Signal Processing Systems10.1007/s11265-021-01671-1Online publication date: 10-Jun-2021
    • (2019)Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron ClassifiersIntegrated Uncertainty in Knowledge Modelling and Decision Making10.1007/978-3-030-14815-7_25(295-307)Online publication date: 7-Mar-2019

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