Unsupervised context switch for classification tasks on data streams with recurrent concepts

Abstract

In this paper, we propose a novel approach to deal with concept drifts in data streams. We assume we can collect labeled data for different concepts in the training phase; however, in the test phase, no labels are available. Our approach consists of the storage of a limited number of classification models and the unsupervised identification of the most suitable one depending on the current concept. Several real-world classification problems with extreme label latency can use this setting. One example is the identification of insects species using wing-beat data gathered by sensors in field conditions. Flying insects have their wing-beat frequency indirectly affected by temperature, among other factors. In this work, we show that we can dynamically identify which is the most appropriate classification model, among other models from data with different temperature conditions, without any temperature information. We then expand the use of the method to other data sets and obtain accurate results.