afe: Compute Average Feature Entropy (AFE)
In wordmap: Feature Extraction and Document Classification with Noisy Labels
afe R Documentation
Compute Average Feature Entropy (AFE)
Description
afe() computes Average Feature Entropy (AFE), which measures randomness of
occurrences of features in labelled documents (Watanabe & Zhou, 2020). In
creating seed dictionaries, AFE can be used to avoid adding seed words that would
decrease classification accuracy.
Usage
afe(x, y, smooth = 1)
Arguments
x
a dfm for features.
y
a dfm for labels.
smooth
a numeric value for smoothing to include all the features.
Value
Returns a single numeric value.
References
Watanabe, Kohei & Zhou, Yuan (2020). "Theory-Driven Analysis of
Large Corpora: Semisupervised Topic Classification of the UN Speeches".
doi:10.1177/0894439320907027. Social Science Computer Review.
wordmap documentation built on Oct. 21, 2024, 1:07 a.m.
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| afe | R Documentation |
Compute Average Feature Entropy (AFE)
Description
afe() computes Average Feature Entropy (AFE), which measures randomness of
occurrences of features in labelled documents (Watanabe & Zhou, 2020). In
creating seed dictionaries, AFE can be used to avoid adding seed words that would
decrease classification accuracy.
Usage
afe(x, y, smooth = 1)
Arguments
x |
a dfm for features. |
y |
a dfm for labels. |
smooth |
a numeric value for smoothing to include all the features. |
Value
Returns a single numeric value.
References
Watanabe, Kohei & Zhou, Yuan (2020). "Theory-Driven Analysis of Large Corpora: Semisupervised Topic Classification of the UN Speeches". doi:10.1177/0894439320907027. Social Science Computer Review.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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