Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine Learning
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to form dynamic committees
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Classi.cation of Examples by Multiple Agents with Private Features
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A Comparative Analysis of Negotiation Methods for a Multi-neural Agent System
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Learning collaboration strategies for committees of learning agents
Autonomous Agents and Multi-Agent Systems
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Neuro-Fuzzy-Based Agent System with Data Distribution among the Agents for Classification Tasks
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Learning and joint deliberation through argumentation in multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Neural-network feature selector
IEEE Transactions on Neural Networks
Ensembles of ARTMAP-based neural networks: an experimental study
Applied Intelligence
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The ClassAge (classifier agents) system has been proposed as an alternative to transform the centralized decision-making process of a multi-classifier system into a distributed, flexible and incremental one. This system has presented good results in some conventional (centralized) classification tasks. Nevertheless, in some classification tasks, relevant features might be distributed over a set of agents. These applications can be classified as distributed classification tasks and a method for distributing data (features or attributes) among the agents is needed. In this paper, an investigation of the impact of using data distribution among the agents in the performance of ClassAge will be performed. In this investigation, the performance of the ClassAge system will be compared with some existing multi-classifier systems. In all combination systems, a feature distribution method based on the Pearson correlation will be used.