  The goal of the change-point detection is to discover changes of time series
distribution. One of the state of the art approaches of the change-point
detection are based on direct density ratio estimation. In this work we show
how existing algorithms can be generalized using various binary classification
and regression models. In particular, we show that the Gradient Boosting over
Decision Trees and Neural Networks can be used for this purpose. The algorithms
are tested on several synthetic and real-world datasets. The results show that
the proposed methods outperform classical RuLSIF algorithm. Discussion of cases
where the proposed algorithms have advantages over existing methods are also
provided.
