Authors:
Valerio Grossi
1
and
Franco Turini
2
Affiliations:
1
University of Padova, Italy
;
2
University of Pisa, Italy
Keyword(s):
Data Mining, Data Streams Classification, Ensemble Classifier, Concept Drifting.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
The large diffusion of different technologies related to web applications, sensor networks and ubiquitous computing, has introduced new important challenges for the data mining community. The rising need of analyzing data streams introduces several requirements and constraints for a mining system. This paper analyses a set of requirements related to the data streams environment, and proposes a new adaptive method for data streams classification. The system employs data aggregation techniques that, coupled with a selective ensemble approach, perform the classification task. The approach adopts the behaviour of the selective ensemble by dynamically updating the threshold for enabling the classifiers. The system is explicitly conceived to satisfy these requirements even in the presence of concept drifting.