CS-ARF: Compressed Adaptive Random Forests for Evolving Data Stream Classification
Maroua Bahri, Heitor Murilo Gomes, Albert Bifet, Silviu Maniu
Abstract
Ensemble-based methods are one of the most often used methods in the classification task that have been adapted to the stream setting because of their high learning performance achievement. For instance, Adaptive Random Forests (ARF) is a recent ensemble method for evolving data streams that proved to be of a good predictive performance but, as all ensemble methods, it suffers from a severe drawback related to the high computational demand which prevents it from being efficient and further exacerbates with high-dimensional data. In this context, the application of a dimensionality reduction technique is crucial while processing the Internet of Things (IoT) data stream with ultrahigh dimensionality. In this paper, we aim to alleviate this deficiency and improve ARF performance, so we introduce the CS-ARF approach that uses Compressed Sensing (CS) as an internal pre-processing task, to reduce the dimensionality of data before starting the learning process, that will potentially lead to a meaningful improvement in memory usage. Experiments on various datasets show the high classification performance of our CS-ARF approach compared against current state-of-the-art methods while reducing resource usage.