ArticleEntropy (Basel, Switzerland)2020
Weighted Mean Squared Deviation Feature Screening for Binary Features.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Patient No-Show Prediction: A Systematic Literature Review.Entropy (Basel, Switzerland) · 2020Review
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Authors and funding
2 authors.
Funding
Abstract
In this study, we propose a novel model-free feature screening method for ultrahigh dimensional binary features of binary classification, called weighted mean squared deviation (WMSD). Compared to Chi-square statistic and mutual information, WMSD provides more opportunities to the binary features with probabilities near 0.5. In addition, the asymptotic properties of the proposed method are theoretically investigated under the assumption log p = o ( n ) . The number of features is practically selected by a Pearson correlation coefficient method according to the property of power-law distribution. Lastly, an empirical study of Chinese text classification illustrates that the proposed method performs well when the dimension of selected features is relatively small.
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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.