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