Abstract: Learning from data streams is a hot topic in machine learning and data mining. In this talk, we present three different problems and discuss streaming techniques to solve them. The first problem is the application of data stream techniques to telecommunications fraud detection. We propose an algorithm for the interconnected by-pass fraud problem. This real-world problem requires processing high-speed telecommunications data and providing fraud alarms in real-time. For the second problem, we present an architecture to explain black-box models for predictive maintenance. The explanations are oriented toward equipment anomalies. For the third problem, we present one of the first algorithms for online hyper-parameter tuning for streaming data. The Self hyper-Parameter Tunning (SPT) algorithm is an optimization algorithm for online hyper-parameter tuning from non-stationary data streams. SPT works as a wrapper over any streaming algorithm and can be used for classification, regression, and recommendation.

Bio: Joao Gama is a Full Professor at the School of Economics, University of Porto, Portugal. He received his Ph.D. in Computer Science from the University of Porto in 2000. He is EurIA Fellow, IEEE Fellow, Fellow of the Asia-Pacific AI Association, and member of the board of directors of the LIAAD, a group belonging to INESC TEC. His h-index on Google Scholar is 67. He is an Editor of several top-level Machine Learning and Data Mining journals. He is ACM Distinguish Speaker. He served as Program Chair of ECMLPKDD 2005, DS09, ADMA09, EPIA 2017, DSAA 2017, served as Conference Chair of IDA 2011, ECMLPKDD 2015, DSAA'2021, and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD, and ACM SIGAPP. His main research interests are in knowledge discovery from data streams, evolving data, probabilistic reasoning, and causality. He published more than 300 reviewed papers in journals and major conferences. He has an extensive list of publications in data stream learning.