Gabriel Graph Transductive Approach to Dataset Shift

It is not uncommon for data obtained from systems to change after the model is learned. These occurrences are named dataset shifts and to deal with them models with the ability to adapt to data changes must be used. A strategy that can be easily integrated to other classifiers is proposed. It creates a geometrical representation of data that extracts information from both labelled and unlabelled data. Then data entropy and Jensen-Shannon dissimilarity tests are used during the model selection to handle cases where data shift. Results have shown that the proposed method is promising because of its simple integration with state of art classifiers and its performance in enhancing said classifiers accuracy in the studied cases.