ArticleNeurocomputing2021
Region of Interest Selection for Functional Features.
Article in Neurocomputing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.Theranostics · 2024Article
- Harnessing Machine Learning for Prediction of Postoperative Pulmonary Complications: Retrospective Cohort Design.Journal of clinical medicine · 2023Article
- Machine learning-based obesity classification considering 3D body scanner measurements.Scientific reports · 2023Article
- S2FLNet: Hepatic steatosis detection network with body shape.Computers in biology and medicine · 2022Article
- Article
- Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.Journal of biomedical informatics · 2021Article
- Decoding different working memory states during an operation span task from prefrontal fNIRS signals.Biomedical optics express · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Feature selection is a critical component in supervised learning to improve model performance. Searching for the optimal feature candidates can be NP-hard. With limited data, cross-validation is widely used to alleviate overfitting, which unfortunately suffers from high computational cost. We propose a highly innovative strategy in feature selection to reduce the overfitting risk but without cross-validation. Our method selects the optimal sub-interval, i.e., region of interest (ROI), of a functional feature for functional linear regression where the response is a scalar and the predictor is a function. For each candidate sub-interval, we evaluate the overfitting risk by calculating a necessary sample size to achieve a pre-specified statistical power. Combining with a model accuracy measure, we rank these sub-intervals and select the ROI. The proposed method has been compared with other state-of-the-art feature selection methods on several reference datasets. The results show that our proposed method achieves an excellent performance in prediction accuracy and reduces computational cost substantially.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.