singleIter {AdaSampling} | R Documentation |
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)
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, |
sampleFactor |
provides a control on the sample size for resampling. |
seed |
sets the seed. |
weights |
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