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2019 | OriginalPaper | Buchkapitel

A Principled Two-Step Method for Example-Dependent Cost Binary Classification

verfasst von : Javier Mediavilla-Relaño, Aitor Gutiérrez-López, Marcelino Lázaro, Aníbal R. Figueiras-Vidal

Erschienen in: From Bioinspired Systems and Biomedical Applications to Machine Learning

Verlag: Springer International Publishing

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Abstract

This paper presents a principled two-step method for example-dependent cost binary classification problems. The first step obtains a consistent estimate of the posterior probabilities by training a Multi-Layer Perceptron with a Bregman surrogate cost. The second step uses the provided estimates in a Bayesian decision rule. When working with imbalanced datasets, neutral re-balancing allows getting better estimates of the posterior probabilities. Experiments with real datasets show the good performance of the proposed method in comparison with other procedures.

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Nächstes Kapitel Symbiotic Autonomous Systems with Consciousness Using Digital Twins

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Titel: A Principled Two-Step Method for Example-Dependent Cost Binary Classification
verfasst von: Javier Mediavilla-Relaño
Aitor Gutiérrez-López
Marcelino Lázaro
Aníbal R. Figueiras-Vidal
Verlag: Springer International Publishing
Buch: From Bioinspired Systems and Biomedical Applications to Machine Learning
Print ISBN: 978-3-030-19650-9

Electronic ISBN: 978-3-030-19651-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-19651-6_2

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