An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning

Ryan A. Elwell AN ENSEMBLE-BASED COMPUTATIONAL APPROACH FOR INCREMENTAL LEARNING IN NON-STATIONARY ENVIRONMENTS RELATED TO SCHEMAAND SCAFFOLDING-BASED HUMAN LEARNING 2008/09 Robi Polikar, Ph.D. Master of Science in Engineering The principal dilemma in a learning process, whether human or computer, is adapting to new information, especially in cases where this new information conflicts with what was previously learned. The design of computer models for incremental learning is an emerging topic for classification and prediction of large-scale data streams undergoing change in underlying class distributions (definitions) over time; yet currently, they often ignore significant foundational learning theory that has been developed in the domain of human learning. This shortfall leads to many deficiencies in the ability to organize existing knowledge and to retain relevant knowledge for long periods of time. In this work, we introduce a unique computer-learning algorithm for incremental knowledge acquisition using an ensemble of classifiers, Learn". NSE (Non-Stationary Environments), specifically for the case where the nature of knowledge to be learned is evolving. Learn++. NSE is a novel approach to evaluating and organizing existing knowledge (classifiers) according to the most recent data environment. Under this architecture, we address the learning problem at both the learner and supervisor end, discussing and implementing three main approaches: knowledge weighting/organization, forgetting prior knowledge, and change/drift detection. The framework is evaluated on a variety of canonical and real-world data streams (weather prediction, electricity price prediction, and spam detection). This study reveals the catastrophic effect of forgetting prior knowledge, supporting the organization technique proposed by Learn++. NSE as the most consistent performer during various drift scenarios, while also addressing the sheer difficulty in designing a system that strikes a balance between maintaining all knowledge and making decisions based only on relevant knowledge, especially in severe, unpredictable environments which are often encountered in the real-world. ACKNOWLEDGEMENTS First of all, I would like to thank my advisor Dr. Robi Polikar for providing invaluable technical and intellectual support and encouragement throughout my education and research. Special thanks are also due to Michael Muhlbaier, who is responsible for the conception of the Learn++. NSE algorithm, and to Matthew Karnick for his helpful assistance in Matlab coding and understanding fundamentals of our research. To my fellow graduate students in the Signal Processing & Pattern Recognition Laboratory and virtual reality labs, I thank you for your friendship, assistance, and generally for keeping me sane. I thank my parents for their continued love and support in everything nontechnical. I am also grateful for the Rowan University ECE department faculty. Specifically, the following deserve special thanks for showing their genuine care about my engineering future: Dr. Peter Jansson, Dr. Ravi Ramachandran, Dr. Linda Head, and Dr. Shreekanth Mandayam. I thank the Dean's Office staff and the College of Engineering faculty for their regard for my well-being which has extended even beyond my education, even to my personal needs. Finally, I attribute all my accomplishments to my Father God in heaven, who has blessed me not only with success, but also with all my abilities. He gives me comfort not only in this life, but also thereafter because of His Son Jesus Christ, who has made the way for my salvation and eternal life through His life, death, and resurrection. Furthermore, by His Spirit, I have all the motivation to pursue research in engineering, for "I can do all things through Him who strengthens me" (Philippians 4:13).