From Data to Knowledge - 206 - Joao Gama
Channel: ckleinastro
Date: 08/05/2012

Slides: http://lyra.berkeley.edu/CDIConf/pdfs/JGama-2012.pdf

Joao Gama: "Challenges on Mining Evolving Data Streams". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012).

Abstract
Joao Gama (Lab. of A.I. and Decision Support, Economics at Univ. of Porto, Portugal)
The computational model of data streams imposes new challenges and open new research opportunities on the design of data mining algorithms. Data is abundant, being continuously generated from time-changing processes with unknown dynamics. Evolving time-changing data requires that learning algorithms must be able to monitor the evolution of the learning process. Monitoring the learning process opens the ability of predictive self-diagnosis; not only after a failure has occurred, but also predictive, before the failure. These aspects require monitoring the evolution of the learning process, taking into account the available resources. Diagnosis is a significant and useful characteristic, and requires the ability of reasoning and learning about the learning process itself. In this talk we present a one-pass classification algorithm able for self-diagnosis. It is able to detect and react to changes in the process generating data, identifies contexts using drift detection, characterize contexts using meta-learning, and select the most appropriate base model for the incoming data using unlabeled examples.