An Improved Version of the Fuzzy Set Based Evolving Modeling with Multitask Learning

This paper introduces two novel contributions to the online learning algorithm called Fuzzy set Based evolving Modeling with Multitask Learning (FBeM_MTL), the first algorithm in the literature to consider multitask learning in the context of data stream, adaptive and evolving systems. In this new version, the degree of intersection of the information granules is directly used to define a real-valued matrix representing the relationship among the learning tasks, responsible for defining the parameters of the consequent part of all functional IF-THEN fuzzy rules. Unlike the original FBeM_MTL, in this new version, we eliminated the need for the binarization of the matrix representing the connected rules, guiding to both performance improvement and reduction in the number of user-defined parameters. The second contribution is the adoption of the Weighted Least Squares (WLS) method to define the parameters of the consequent part of the rules, using the similarity measure between every pair of samples to the mean point to set their corresponding weights in the WLS problem. Computational experiments on time series prediction of weather temperature, rain precipitation, wind speed in eolian farms and stock exchange are used to validate the performance of this new version. When compared to the original FBeM_MTL and also to several other state-of-the-art evolving systems in the literature, our approach guides to competitive results using a reduced number of parameters.