singleIter: 'singleIter()' applies a single iteraction of AdaSampling...

In AdaSampling: Adaptive Sampling for Positive Unlabeled and Label Noise Learning

Description

Classification algorithms included are support vector machines (svm), k-nearest neighbours (knn), logistic regression (logit), linear discriminant analysis (lda), feature weighted knn (wKNN).

Usage

singleIter(Ps, Ns, dat, test = NULL, pos.probs = NULL,
  una.probs = NULL, classifier = "svm", sampleFactor, seed, weights)

Arguments

Ps	names (name as index) of positive examples
Ns	names (name as index) of negative examples
dat	training data matrix, without class labels.
test	test data matrix, without class labels. Training data matrix will be used for testing if this is NULL (default).
pos.probs	a numeric vector of containing probability of positive examples been positive
una.probs	a numeric vector of containing probability of negative or unannotated examples been negative
classifier	classification algorithm to be used for learning. Current options are support vector machine, "svm", k-nearest neighbour, "knn", logistic regression "logit", linear discriminant analysis "lda", and feature weighted knn, "wKNN".
sampleFactor	provides a control on the sample size for resampling.
seed	sets the seed.
weights	feature weights, required when using weighted knn.

References

Yang, P., Liu, W., Yang. J. (2017) Positive unlabeled learning via wrapper-based adaptive sampling. International Joint Conferences on Artificial Intelligence (IJCAI), 3272-3279

Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J.(2018) AdaSampling for positive-unlabeled and label noise learning with bioinformatics applications. IEEE Transactions on Cybernetics, doi:10.1109/TCYB.2018.2816984

AdaSampling documentation built on May 21, 2019, 9:02 a.m.