Workshop on Learning in the Presence of Class Imbalance and Concept Drift
Monitoring the Learning Process
Dr. João Gama, LIAAD-INESC Porto
Data Mining is faced with new challenges. In emerging applications (like financial data, traffic TCP/IP, sensor networks, etc) data continuously flow eventually at high speed. The processes generating data evolve over time, and the concepts we are learning change. In this talk we present a one-pass classification algorithm able to detect and react to changes. We present a framework that identify contexts using drift detection, characterise contexts using meta-learning, and select the most appropriate base model for the incoming data using unlabelled examples.
Evolving data requires that learning algorithms must be able to monitor the learning process and the ability of predictive self-diagnosis. A significant and useful characteristic is diagnostics - not only after failure has occurred, but also predictive (before failure). These aspects require monitoring the evolution of the learning process, taking into account the available resources, and the ability of reasoning and learning about it.
Dr. João Gama is an Associate Professor at the University of Porto, Portugal. He is also a senior researcher and member of the board of directors of the Laboratory of Artificial Intelligence and Decision Support (LIAAD), a group belonging to INESC Porto. João Gama serves as the member of the Editorial Board of Machine Learning Journal, Data Mining and Knowledge Discovery, Intelligent Data Analysis and New Generation Computing. He served as Co-chair of ECML 2005, DS09, ADMA09 and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD. He was also the chair for the conference of Intelligent Data Analysis 2011. He has given 7 keynotes and 2 plenary talks. His main research interest is in knowledge discovery from data streams and evolving data. He is the author of a recent book on Knowledge Discovery from Data Streams. He has extensive publications in the area of data stream learning.
Recent Progress in AI: From Perceiving, Learning and Reasoning to Behaving
Prof. Dacheng Tao, University of Sydney, Australia
Since the concept of Turing machine has been first proposed in 1936, the capability of machines to perform intelligent tasks went on growing exponentially. Artificial Intelligence (AI), as an essential accelerator, pursues the target of making machines as intelligent as human beings. It has already reformed how we live, work, learning, discover and communicate. In this talk, I will review our recent progress on AI by introducing some representative advancements from algorithms to applications, and illustrate the stairs for its realization from perceiving to learning, reasoning and behaving. To push AI from the narrow to the general, many challenges lie ahead. I will bring some examples out into the open, and shed lights on our future target. Today, we teach machines how to be intelligent as ourselves. Tomorrow, they will be our partners to get into our daily life.
Prof. Dacheng Tao is Professor of Computer Science and ARC Future Fellow in the School of Information Technologies and the Faculty of Engineering and Information Technologies at The University of Sydney. He was Professor of Computer Science and Director of the Centre for Artificial Intelligence in the University of Technology Sydney. He mainly applies statistics and mathematics to Artificial Intelligence and Data Science. His research interests spread across computer vision, data science, image processing, machine learning, and video surveillance. His research results have expounded in one monograph and 500+ publications at top journals and conferences, such as IEEE T-PAMI, T-NNLS, T-IP, JMLR, IJCV, IJCAI, AAAI, NIPS, ICML, CVPR, ICCV, ECCV, ICDM; and ACM SIGKDD, with several best paper awards, such as the best theory/algorithm paper runner up award in IEEE ICDM’07, the best student paper award in IEEE ICDM’13, and the 2014 ICDM 10-year highest-impact paper award. He received the 2015 Australian Scopus-Eureka Prize, the 2015 ACS Gold Disruptor Award and the 2015 UTS Vice-Chancellor’s Medal for Exceptional Research. He is a Fellow of the IEEE, OSA, IAPR and SPIE.