Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Active learning methods increase classification quality by means of user feedback. An
important subcategory is active learning for outlier detection with one-class classifiers. While …

Selecting informative data points for expert feedback can significantly improve the
performance of anomaly detection (AD) in various contexts, such as medical diagnostics or …

Implicit feedback data is extensively explored in recommendation as it is easy to collect and
generally applicable. However, predicting users' preference on implicit feedback data is a …

Active learning has been utilized as an efficient tool in building anomaly detection models by
leveraging expert feedback. In an active learning framework, a model queries samples to be …

In a broad range of fields it may be desirable to reuse a supervised classification algorithm
and apply it to a new data set. However, generalization of such an algorithm and thus …

Active learning is a common solution for reducing labeling costs and maximizing the impact
of human labeling efforts in binary and multi-class classification settings. However, when we …

Traditional time series classification problem with supervised learning algorithm needs a
large set of labeled training data. In reality, the number of labeled data is often smaller and …

Estimating the proportion of positive examples (ie, the class prior) from positive and
unlabeled (PU) data is an important task that facilitates learning a classifier from such data …

Active learning for outlier detection involves users in the process, by asking them for
annotations of observations, in the form of class labels. The usual assumption is that users …