Tabular biomedical data is often high-dimensional but with a very small number of samples. Although recent work showed that well-regularised simple neural networks could outperform more sophisticated architectures on tabular data, they are still prone to overfitting on tiny datasets with many potentially irrelevant features. To combat these issues, we propose Weight Predictor Network with Feature Selection (WPFS) for learning neural networks from high-dimensional and small sample data by reducing the number of learnable parameters and simultaneously performing feature selection. In addition to the classification network, WPFS uses two small auxiliary networks that together output the weights of the first layer of the classification model. We evaluate on nine real-world biomedical datasets and demonstrate that WPFS outperforms other standard as well as more recent methods typically applied to tabular data. Furthermore, we investigate the proposed feature selection mechanism and show that it improves performance while providing useful insights into the learning task.

Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data / Margeloiu, A.; Simidjievski, N.; Lio, P.; Jamnik, M.. - 37:(2023), pp. 9081-9089. (Intervento presentato al convegno 37th AAAI Conference on Artificial Intelligence, AAAI 2023 tenutosi a Washington; usa) [10.1609/aaai.v37i8.26090].

Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data

Lio P.;
2023

Abstract

Tabular biomedical data is often high-dimensional but with a very small number of samples. Although recent work showed that well-regularised simple neural networks could outperform more sophisticated architectures on tabular data, they are still prone to overfitting on tiny datasets with many potentially irrelevant features. To combat these issues, we propose Weight Predictor Network with Feature Selection (WPFS) for learning neural networks from high-dimensional and small sample data by reducing the number of learnable parameters and simultaneously performing feature selection. In addition to the classification network, WPFS uses two small auxiliary networks that together output the weights of the first layer of the classification model. We evaluate on nine real-world biomedical datasets and demonstrate that WPFS outperforms other standard as well as more recent methods typically applied to tabular data. Furthermore, we investigate the proposed feature selection mechanism and show that it improves performance while providing useful insights into the learning task.
2023
37th AAAI Conference on Artificial Intelligence, AAAI 2023
Neural Network; Deep Learning; Image Classification
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data / Margeloiu, A.; Simidjievski, N.; Lio, P.; Jamnik, M.. - 37:(2023), pp. 9081-9089. (Intervento presentato al convegno 37th AAAI Conference on Artificial Intelligence, AAAI 2023 tenutosi a Washington; usa) [10.1609/aaai.v37i8.26090].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1726842
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