Keep it Simple: Handcrafting Feature and Tuning Random Forests and XGBoost to face the Affective Movement Recognition Challenge 2021

D’Amato, Vincenzo; Oneto, Luca; Camurri, Antonio; Anguita, Davide

doi:10.1109/aciiw52867.2021.9666428

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…For this purpose, we will leverage on a Simple Informed Machine Learning (ML) based model [2][3][4][5]. As we will describe later, our problem is characterized by two main issues: data regarding faults are scarce and the resulting dataset is strongly unbalanced.…”

Section: Introductionmentioning

confidence: 99%

Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets

Oneto¹,

Minisi²,

Garrone³

et al. 2022

ESANN 2022 Proceedings

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Signals, track circuits, switches, and relay rooms are simultaneously the most critical and most maintained railway assets. A fault of one of these assets may strongly reduce the railway network capacity or even disrupt the circulation. Effectively predicting what assets may need maintenance allows to anticipate the intervention thus avoiding a failure. Currently, this problem is tackled by infrastructure managers mostly relying on operators' experience and with limited support of decision supporting tools. In this paper, we propose a Simple Informed Machine Learning (ML) based model able to automatically predict what asset need to be maintained fully leveraging on the operator experience. However, ML models in modern industrial MLOps pipelines demand continuous data collection, model re-training, testing, and monitoring, creating a large technical debt. In fact, one of the main requirements of these pipelines is to not be regressive, i.e., not simply improve average performances but also not incorrectly predicting an output that was correctly classified by the reference model (negative flips). In this work we face this problem by empowering the proposed ML with Non Regressive properties. Results on real data coming from a portion of an Italian Railway Network managed by Rete Ferroviaria Italiana, the Italian Infrastructure Manager, will support our proposal.

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Section: Introductionmentioning

confidence: 99%

Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets

Oneto¹,

Minisi²,

Garrone³

et al. 2022

ESANN 2022 Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment

Garrone

Minisi²,

Oneto

et al. 2022

Lecture Notes in Networks and Systems

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Pain Recognition With Physiological Signals Using Multi-Level Context Information

et al. 2023

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Automatic pain recognition is essential in healthcare. In previous studies, automatic pain recognition methods preferentially apply the features extracted from physiological signals for conventional models. This method provides good performance but mainly relies on medical expertise for feature extraction of physiological signals. This paper presents a deep learning approach based on physiological signals that have the role of both feature extraction and classification, regardless of medical expertise. We propose multi-level context information for each physiological signal discriminating between pain and painlessness. Our experimental results prove that multi-level context information has more significant performances than uni-level context information based on Part A of the BioVid Heat Pain database and the Emopain 2021 dataset. For Part A of the BioVid Heat Pain database, our experimental results for pain recognition tasks include: Pain 0 and Pain 1, Pain 0 and Pain 2, Pain 0 and Pain 3, and Pain 0 and Pain 4. In the classification task between Pain 0 and Pain 4, the results achieve an average accuracy of 84.8 ± 13.3% for 87 subjects and 87.8 ± 11.4% for 67 subjects in a Leave-One-Subject-Out cross-validation evaluation. The proposed method adopts the ability of deep learning to outperform conventional methods on physiological signals. INDEX TERMS pain recognition, physiological signals, context vector, attention module, deep learning.

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Keep it Simple: Handcrafting Feature and Tuning Random Forests and XGBoost to face the Affective Movement Recognition Challenge 2021

Cited by 5 publications

References 39 publications

Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets

Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets

Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment

Pain Recognition With Physiological Signals Using Multi-Level Context Information

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