Kernel-based selective ensemble learning for streams of trees

Authors:
Valerio Grossi;Alessandro Sperduti
Affiliations:
Department of Pure and Applied Matematics, University of Padova;Department of Pure and Applied Matematics, University of Padova
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Year:
2011

Citing 17
Cited 0

Mining high-speed data streams

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Online Algorithms for Mining Semi-structured Data Stream

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Mining data streams: a review

ACM SIGMOD Record
Discretization from data streams: applications to histograms and data mining

Proceedings of the 2006 ACM symposium on Applied computing
Fast On-line Kernel Learning for Trees

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Learning drifting concepts: Example selection vs. example weighting

Intelligent Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts

The Journal of Machine Learning Research
Mining adaptively frequent closed unlabeled rooted trees in data streams

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Info-fuzzy algorithms for mining dynamic data streams

Applied Soft Computing
New ensemble methods for evolving data streams

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive XML Tree Classification on Evolving Data Streams

ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Handling numeric attributes in hoeffding trees

PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Stream mining: a novel architecture for ensemble-based classification

Knowledge and Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Learning from streaming data represents an important and challenging task. Maintaining an accurate model, while the stream goes by, requires a smart way for tracking data changes through time, originating concept drift. One way to treat this kind of problem is to resort to ensemble-based techniques. In this context, the advent of new technologies related to web and ubiquitous services call for the need of new learning approaches able to deal with structured-complex information, such as trees. Kernel methods enable the modeling of structured data in learning algorithms, however they are computationally demanding. The contribute of this work is to show how an effective ensemble-based approach can be deviced for streams of trees by optimizing the kernel-based model representation. Both efficacy and efficiency of the proposed approach are assessed for different models by using data sets exhibiting different levels and types of concept drift.