Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The distributed boosting algorithm
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Data Gathering Algorithms in Sensor Networks Using Energy Metrics
IEEE Transactions on Parallel and Distributed Systems
Machine Learning
Learning Ensembles from Bites: A Scalable and Accurate Approach
The Journal of Machine Learning Research
Combs, needles, haystacks: balancing push and pull for discovery in large-scale sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Lightweight detection and classification for wireless sensor networks in realistic environments
Proceedings of the 3rd international conference on Embedded networked sensor systems
Deploying a Wireless Sensor Network on an Active Volcano
IEEE Internet Computing
SENDROM: sensor networks for disaster relief operations management
Wireless Networks
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
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Wireless sensor networks promise an unprecedented opportunity to monitor physical environments via inexpensive wireless embedded devices. Given the sheer amount of sensed data, efficient classification of them becomes a critical task in many sensor network applications. The large scale and the stringent energy constraints of such networks however challenge the conventional classification techniques that demand enormous storage space and centralized computation. In this paper, we propose a novel decision-tree-based hierarchical distributed classification approach, in which local classifiers are built by individual sensors and merged along the routing path forming a spanning tree. The classifiers are iteratively enhanced by combining strategically generated pseudo data and new local data, eventually converging to a global classifier for the whole network. We also introduce some control factors to facilitate the effectiveness of our approach. Through extensive simulations, we study the impact of the introduced control factors, and demonstrate that our approach maintains high classification accuracy with very low storage and communication overhead. The approach also addresses a critical issue of heterogeneous data distribution among the sensors.