Description Usage Arguments References

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

1 2 | ```
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

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