Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. There are three main approaches to institute generalization guarantees: (1) by providing bounds of various notions of functional space capacity- most notably, using the VC-dimension; (2) by establishing connections between the stability of a learning algorithm and its ability to generalize, and (3) by considering the compression-scheme method. Here, by bridging ensemble techniques, statistical learning theory, and collaborative principles, we develop stability-guided collaborative ensemble learning methods for classification and regression. The algorithms were tested on various datasets, showing improved performance in both reducing the error rates and reducing the computation time. Paraphrasing Schapire and Freund, who compared the general concept of boosting with “garnering wisdom from a council of fools”, we could add that in our approach “collaborative fools generate even more wisdom”.
Ljupco Kocarev is a full professor at the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia, and research professor at University of California San Diego (UCSD), USA. His scientific interests include network science, machine learning, nonlinear systems and circuits; dynamical systems, mathematical modeling, and computational biology. He has co-authored more than 150 journal papers in 30 different international journals, ranging from mathematics to physics and from electrical engineering to computer sciences. According to Science Citation Index his work has been cited more than 6000 times, while according to Google scholar his work has been cited almost 12000 times. He is a fellow of IEEE, member of the Macedonian Academy of Sciences and Arts, and member of several other academies worldwide.
Website: http://www.cs.manu.edu.mk/people/faculty/ljupco-kocarev