| DOI | Resolve DOI: https://doi.org/10.1109/ICDS62089.2024.10756470 |
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| Author | Search for: Valdés, Julio J.1ORCID identifier: https://orcid.org/0000-0003-2930-0325 |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS), October 23-24, 2024, Marrakech, Morocco |
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| Subject | supervised learning; constructing training/testing sets; sampling; convex decompositions; training; manifolds; machine learning algorithms; prevention and mitigation; heuristic algorithms; hyperparameter optimization; skin; complexity theory; testing |
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| Abstract | The paper presents heuristics and several algorithms for constructing training/testing sets for supervised machine learning based on layered geometric structures (δ -convex decompositions) extracted from input space data. Experiments with well-known datasets from public repositories showed that models derived from training/testing sets constructed via the proposed heuristics closely match or improve upon those using sets coming solely from random sampling. The insights coming from the exploitation of structural information represent a promising way of better using the available data for unsupervised and supervised modeling. Despite the advantages, the presented approaches have limitations and challenges associated with data complexities and geometric degeneracies usually related to the increased dimensionality of input feature spaces. The study discusses these issues and includes explorations of mitigation strategies based on working with lower dimensional manifold data representations with promising results. This is a preliminary study and future work exploring these concepts and approaches is necessary. |
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| Publication date | 2024-10-23 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | dfe26f80-0d19-40e4-b4db-fd0d692f9798 |
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| Record created | 2024-12-02 |
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| Record modified | 2024-12-04 |
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