Incremental Learning From Stream Data

Authors:
Haibo He;Sheng Chen;Kang Li;Xin Xu
Affiliations:
Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA;School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, U.K.;Institute of Automation, College of Mechatronics and Automation, National University of Defense Technology, Changsha, China
Venue:
IEEE Transactions on Neural Networks - Part 1
Year:
2011

Citing 0
Cited 4

A hierarchical neural network architecture for classification

ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
On the effectiveness of application-aware self-management for scientific discovery in volunteer computing systems

SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
A novel online boosting algorithm for automatic anatomy detection

Machine Vision and Applications
The CART decision tree for mining data streams

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.