| Title: | MsGEN : measuring generalization of nutrient value prediction across different recipe datasets |
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| Authors: | ID Ispirova, Gordana, Institut Jožef Stefan (Author)
ID Eftimov, Tome, Institut Jožef Stefan (Author)
ID Džeroski, Sašo, Institut Jožef Stefan (Author)
ID Koroušić-Seljak, Barbara, Institut Jožef Stefan (Author) |
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| Files: |  URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S0957417423020092?via%3Dihub
 PDF - Presentation file, download (3,27 MB)
MD5: 590CD5C276E2F45AADC6BCF60EF204BB
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | In this study, we estimate the generalization of the performance of previously proposed predictive models for nutrient value prediction across different recipe datasets. For this purpose, we introduce a quantitative indicator that determines the level of generalization of using the developed predictive model for new unseen data not presented in the training process. On a predefined corpus of recipe embeddings from six publicly available recipe datasets (i.e., projecting them in the same meta-feature vector space), we train predictive models on one of the six recipe datasets and test the models on the rest of the datasets. In parallel, we define and calculate generalizability indexes which are numbers that indicate how generalizable a predictive model is i.e., how well will a predictive model learned on one dataset perform on another one not involved in the training. The evaluation results prove the validity of these indexes – their relation with the accuracy of the predictions. Further, we define three sampling techniques for selecting representative data instances that will cover all parts from the feature space uniformly (involving data from all datasets) and further will improve the generalization of a predictive model. We train predictive models with these generalized datasets and test them on instances from the six recipe datasets that are not selected and included in the generalized datasets. The results from the evaluation of these predictive models show improvement compared to the results from the predictive models trained on one recipe dataset and tested on the others separately. |
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| Keywords: | ML pipeline, predictive modeling, nutrient prediction, recipe datasets |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 18.08.2023 |
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| Article acceptance date: | 06.09.2023 |
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| Publication date: | 16.09.2023 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 1-40 |
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| Numbering: | Vol. , article no. 121507 |
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| Source: | Nizozemska |
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| PID: | 20.500.12556/DiRROS-17082  |
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| UDC: | 004.8 |
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| ISSN on article: | 1873-6793 |
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| DOI: | 10.1016/j.eswa.2023.121507 |
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| COBISS.SI-ID: | 165116419 |
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| Copyright: | © 2023 The Authors. Published by Elsevier Ltd. |
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| Note: | Nasl. z nasl. zaslona;
Soavtorji: Tome Eftimov, Sašo Džeroski, Barbara Koroušić Seljak;
Opis vira z dne 20. 9. 2023;
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| Publication date in DiRROS: | 25.09.2023 |
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| Views: | 733 |
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| Downloads: | 371 |
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