Classification model for data streams based on similarity

Authors:
Dayrelis Mena Torres;Jesús Aguilar Ruiz;Yanet Rodríguez Sarabia
Affiliations:
University of Pinar del Río, Cuba;University "Pablo de Olavide", Spain;Central University of Las Villas "Marta Abreu", Cuba
Venue:
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Year:
2011

Citing 13
Cited 1

Toward memory-based reasoning

Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms

Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching

Artificial Intelligence Review - Special issue on lazy learning
Mining high-speed data streams

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning from Noisy Data

Machine Learning
Detecting Concept Drift with Support Vector Machines

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Accurate decision trees for mining high-speed data streams

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient decision tree construction on streaming data

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Forest trees for on-line data

Proceedings of the 2004 ACM symposium on Applied computing
Efficient instance-based learning on data streams

Intelligent Data Analysis
Improved heterogeneous distance functions

Journal of Artificial Intelligence Research
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning

IEEE Transactions on Evolutionary Computation

A similarity-based approach for data stream classification

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mining data streams is a field of study that poses new challenges. This research delves into the study of applying different techniques of classification of data streams, and carries out a comparative analysis with a proposal based on similarity; introducing a new form of management of representative data models and policies of insertion and removal, advancing also in the design of appropriate estimators to improve classification performance and updating of the model.