Package: AdaSampling 1.3

AdaSampling: Adaptive Sampling for Positive Unlabeled and Label Noise Learning

Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) <doi:10.1109/TCYB.2018.2816984>.

Authors:Pengyi Yang

AdaSampling_1.3.tar.gz
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AdaSampling_1.3.tgz(r-4.6-any)AdaSampling_1.3.tgz(r-4.5-any)
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AdaSampling_1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
AdaSampling/json (API)

# Install 'AdaSampling' in R:

install.packages('AdaSampling', repos = c('https://pyanglab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pengyiyang/adasampling/issues

Datasets:

brca - Wisconsin Breast Cancer Database

On CRAN:

5.11 score 10 stars 13 scripts 538 downloads 1 mentions 4 exports 73 dependencies

Last updated from:c815f1bf8d. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	166
source / vignettes	OK	209
linux-release	OK	169
macos-release	OK	108
macos-oldrel	OK	112
windows-devel	OK	122
windows-release	OK	113
windows-oldrel	OK	130
wasm-release	OK	102

Exports:adaSample adaSvmBenchmark singleIter weightedKNN

Dependencies:caret class cli clock codetools cpp11 data.table diagram digest dplyr e1071 farver foreach future future.apply generics ggplot2 globals glue gower gtable hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp recipes reshape2 rlang rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

Breast cancer classification with AdaSampling

Rendered fromvignette.Rmdusingknitr::rmarkdown

Last update: 2018-06-10
Started: 2018-05-31

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Implementation of AdaSampling for positive unlabelled and label noise learning.	adaSample
Benchmarking AdaSampling efficacy on noisy labelled data.	adaSvmBenchmark
Wisconsin Breast Cancer Database (1991)	brca
'singleIter()' applies a single iteraction of AdaSampling procedure. It returns the probabilities of all samples as being a positive (P) or negative (N) instance, as a two column data frame.	singleIter
Implementation of a feature weighted k-nearest neighbour classifier.	weightedKNN