University of Konstanz
Graduiertenkolleg / PhD Program
Computer and Information Science

Colloquium of the Department and the PhD Program


Adaptive Learning from Drifting Data


Dr. Indrė Žliobaitė, Bournemouth University, UK

date & place

Wednesday, 18.01.2012, 16:15 h
Room C 252


Changing distribution of data over time (concept drift) is one of the major challenges for data mining applications, including marketing, financial analysis, recommender systems, spam categorization and more. As data arrives and evolves over time, constant manual adjustment of models is inefficient and with increasing amounts of data is quickly becoming infeasible. In such situations decision models need to have mechanisms to update or retrain themselves using recent data, otherwise their accuracy will degrade. Research attention to such supervised learning scenarios has been rapidly increasing in the last decade, a lot of adaptive learning models for massive data streams and smaller scale sequential learning problems have been developed. In the first part of the talk we will overview settings and techniques for handling concept drift in supervised learning. We will also outline the next upcoming challenges for handling concept drift. In the second part of the talk we will discuss two extensions of the settings in more detail: active learning from drifting data and adaptive data pre-processing.