Beside SVM, what are the classification models that can be trained by a dataset of only positive training examples? and which of these models are generally known to perform better in such cases?
UPDATE: I mean problems that are described by the following quoted sentences:
- "One-class SVM is an unsupervised algorithm that learns a decision function for novelty detection: classifying new data as similar or different to the training set".
- "But what if you only have data of one class and the goal is to test new data and found out whether it is alike or not like the training data?".
Best Answer
There are plenty possibilities to construct one-class-classifiers. I wrote a number of simple algorithms in the context of authorship verification. Here, only positive samples of one author X are given, so that the task is to judge if a given document was written by X or not. However, it can be adapted to other fields besides authorship verification by just adjusting the features. Here are two of my papers:
Oren Halvani, Lukas Graner, Inna Vogel. Authorship Verification in the Absence of Explicit Features and Thresholds In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science, vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_34
O. Halvani and M. Steinebach, "An Efficient Intrinsic Authorship Verification Scheme Based on Ensemble Learning," 2014 Ninth International Conference on Availability, Reliability and Security, 2014, pp. 571-578, doi: 10.1109/ARES.2014.84.