June 29 11:25AMLearning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that continuously evolve over time, run in resource-aware environments, detect and react to changes in the environment generating data. One important issue, not yet conveniently addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. There are no golden standards for assessing performance in non-stationary environments. This paper proposes a general framework for assessing predictive stream learning algorithms. We defend the use of Predictive Sequential methods for error estimate -- the prequential error. The prequential error allows us to monitor the evolution of the performance of models that evolve over time. Nevertheless, it is known to be a pessimistic estimator in comparison to holdout estimates. To obtain more reliable estimators we need some forgetting mechanism. Two viable alternatives are: sliding windows and fading factors. We observe that the prequential error converges to an holdout estimator when estimated over a sliding window or using fading factors. %A similar observation applies for fading factors. We present illustrative examples of the use of prequential error estimators, using fading factors, for the tasks of:
i. assessing performance of a learning algorithm;
ii. comparing learning algorithms;
iii. hypothesis testing using McNemar test; and
iv. change detection using Page-Hinkley test.
In these tasks, the prequential error estimated using fading factors provide reliable estimators. In comparison to sliding windows, fading factors are faster and memory-less, a requirement for streaming applications. This paper is a contribution to a discussion in the good-practices on performance assessment when learning dynamic models that evolve over time.
W02 WORKSHOP: The 3rd International Workshop on Knowledge Discovery from Sensor Data (SensorKDD-2009)
June 28 9:00AMWide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. The Sensor-KDD 2009 workshop solicits papers that describe innovative solutions in offline data mining and/or real-time analysis of sensor or streaming data. Position papers that describe the challenges and requirements for sensor data based knowledge discovery in high-priority application domains, as well as relevant case studies, are particularly encouraged.