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Balancing Complex Signals for Robust Predictive Modeling
Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points. Conventionally, outliers are removed from the training dataset during preprocessing before bui...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706336/ https://www.ncbi.nlm.nih.gov/pubmed/34960557 http://dx.doi.org/10.3390/s21248465 |
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author | Aman, Fazal Rauf, Azhar Ali, Rahman Hussain, Jamil Ahmed, Ibrar |
author_facet | Aman, Fazal Rauf, Azhar Ali, Rahman Hussain, Jamil Ahmed, Ibrar |
author_sort | Aman, Fazal |
collection | PubMed |
description | Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points. Conventionally, outliers are removed from the training dataset during preprocessing before building predictive models. Such models, however, may have poor predictive performance on the unseen testing data involving outliers. In modern machine learning, outliers are regarded as complex signals because of their significant role and are not suggested for removal from the training dataset. Models trained in modern regimes are interpolated (over trained) by increasing their complexity to treat outliers locally. However, such models become inefficient as they require more training due to the inclusion of outliers, and this also compromises the models’ accuracy. This work proposes a novel complex signal balancing technique that may be used during preprocessing to incorporate the maximum number of complex signals (outliers) in the training dataset. The proposed approach determines the optimal value for maximum possible inclusion of complex signals for training with the highest performance of the model in terms of accuracy, time, and complexity. The experimental results show that models trained after preprocessing with the proposed technique achieve higher predictive accuracy with improved execution time and low complexity as compared to traditional predictive modeling. |
format | Online Article Text |
id | pubmed-8706336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87063362021-12-25 Balancing Complex Signals for Robust Predictive Modeling Aman, Fazal Rauf, Azhar Ali, Rahman Hussain, Jamil Ahmed, Ibrar Sensors (Basel) Article Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points. Conventionally, outliers are removed from the training dataset during preprocessing before building predictive models. Such models, however, may have poor predictive performance on the unseen testing data involving outliers. In modern machine learning, outliers are regarded as complex signals because of their significant role and are not suggested for removal from the training dataset. Models trained in modern regimes are interpolated (over trained) by increasing their complexity to treat outliers locally. However, such models become inefficient as they require more training due to the inclusion of outliers, and this also compromises the models’ accuracy. This work proposes a novel complex signal balancing technique that may be used during preprocessing to incorporate the maximum number of complex signals (outliers) in the training dataset. The proposed approach determines the optimal value for maximum possible inclusion of complex signals for training with the highest performance of the model in terms of accuracy, time, and complexity. The experimental results show that models trained after preprocessing with the proposed technique achieve higher predictive accuracy with improved execution time and low complexity as compared to traditional predictive modeling. MDPI 2021-12-18 /pmc/articles/PMC8706336/ /pubmed/34960557 http://dx.doi.org/10.3390/s21248465 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Aman, Fazal Rauf, Azhar Ali, Rahman Hussain, Jamil Ahmed, Ibrar Balancing Complex Signals for Robust Predictive Modeling |
title | Balancing Complex Signals for Robust Predictive Modeling |
title_full | Balancing Complex Signals for Robust Predictive Modeling |
title_fullStr | Balancing Complex Signals for Robust Predictive Modeling |
title_full_unstemmed | Balancing Complex Signals for Robust Predictive Modeling |
title_short | Balancing Complex Signals for Robust Predictive Modeling |
title_sort | balancing complex signals for robust predictive modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706336/ https://www.ncbi.nlm.nih.gov/pubmed/34960557 http://dx.doi.org/10.3390/s21248465 |
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