Feature ranking in hoeffding algorithms for regression
João Duarte, João Manuel Portela da Gama · 2017
Feature selection and feature ranking are two aspects of the same learning task. They are well studied in batch scenarios, but not in the streaming setting. This paper presents a study on feature ranking from data streams in online learning regression models. The main challenge here is the relevance of features might change over time: features relevant in the past might be irrelevant now and vice-versa. We propose three new online feature ranking algorithms designed for Hoeffding algorithms. We have implemented the three methods in AMRules, a streaming regression algorithm to learn model rules. We compare their behaviour experimentally and present the pros and cons of each method.