We propose the FLORA-Multiple Classification (FLORA-MC) online learning algorithm. In particular, we enhance the FLORA algorithm to allow for (a) multiple classification and (b) numerical input values, while improving its concept drift handling capabilities; thus, making it an excellent choice for use in the area of pervasive computing. The multiple classification allows context-aware systems to differentiate between multiple categories instead of taking binary decisions. Support for numerical input values enables the processing of arbitrary sensor inputs beyond nominal data. To provide the capability of concept drift handling, we propose the use of an advanced window adjustment heuristic, which allows FLORAMC to continuously adapt to the user’s behavior, even if her/his preferences change abruptly over time. In combination with the inherent characteristics of online learning algorithms, our scheme is very well suited for realtime application in the area of context-aware/pervasive computing. We describe the design and implementation of FLORA-MC, and evaluate its performance vs. state-of-the-art learning algorithms. We are able to show the superior performance of our algorithm with respect to reaction time and concept drift handling, while maintaining an excellent accuracy.
Our implementation is available here to the research community as a WEKA module:
|WAH as Meta-Learner|