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2021 | OriginalPaper | Chapter

Partially Monotonic Learning for Neural Networks

Authors : Joana Trindade, João Vinagre, Kelwin Fernandes, Nuno Paiva, Alípio Jorge

Published in: Advances in Intelligent Data Analysis XIX

Publisher: Springer International Publishing

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Abstract

The chapter delves into the issue of trustworthiness in machine learning models, focusing on the incorporation of monotonicity to improve interpretability. It introduces a framework for training semi-monotonic neural networks, which can better model real-world relationships where certain features have monotonic effects on the target variable. The approach is evaluated on datasets from telecom services and car sales, demonstrating its ability to enhance model accuracy and robustness. The study also explores the trade-offs between monotonicity and predictive performance, highlighting the importance of balancing these factors through hyperparameter tuning. The novel methodology presented offers a promising solution for integrating domain knowledge into machine learning models, making them more reliable and understandable.

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Footnotes
3
In most real-world problems, including the ones illustrated in this paper, domain expertise is essential to distinguish between true and spurious monotonic relations.
 
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Metadata
Title
Partially Monotonic Learning for Neural Networks
Authors
Joana Trindade
João Vinagre
Kelwin Fernandes
Nuno Paiva
Alípio Jorge
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-74251-5_2

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