Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Papyrus: a system for data mining over local and wide area clusters and super-clusters
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Boosting Algorithms for Parallel and Distributed Learning
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
Extending Learning to Multiple Agents: Issues and a Model for Multi-Agent Machine Learning (MA-ML)
EWSL '91 Proceedings of the European Working Session on Machine Learning
Creating Ensembles of Classifiers
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Performance Controlled Data Reduction for Knowledge Discovery in Distributed Databases
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Clustering classifiers for knowledge discovery from physically distributed databases
Data & Knowledge Engineering
Model Averaging for Prediction with Discrete Bayesian Networks
The Journal of Machine Learning Research
International Journal of Hybrid Intelligent Systems
Distributed feature extraction in a p2p setting: a case study
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Selecting representative examples and attributes by a genetic algorithm
Intelligent Data Analysis
Data mining for agent reasoning: A synergy for training intelligent agents
Engineering Applications of Artificial Intelligence
Distributed prediction from vertically partitioned data
Journal of Parallel and Distributed Computing
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
Particle swarm optimization for prototype reduction
Neurocomputing
Reducing the dimensionality of dissimilarity space embedding graph kernels
Engineering Applications of Artificial Intelligence
A search space reduction methodology for data mining in large databases
Engineering Applications of Artificial Intelligence
Multiagent Framework for Bio-data Mining
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Data Mining and Multi-agent Integration
Data Mining and Multi-agent Integration
EMADS: An extendible multi-agent data miner
Knowledge-Based Systems
The COMPSET algorithm for subset selection
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Two cooperative ant colonies for feature selection using fuzzy models
Expert Systems with Applications: An International Journal
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
Combining Distributed Classifies by Stacking
WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
Agent-based distributed data mining: the KDEC scheme
Intelligent information agents
MALEF: Framework for distributed machine learning and data mining
International Journal of Intelligent Information and Database Systems
Distributed data mining system based on multi-agent communication mechanism
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Adaptive integrated image segmentation and object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
International Journal of Intelligent Engineering Informatics
Distributed learning with data reduction
Transactions on computational collective intelligence IV
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
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In this paper an agent-based distributed learning framework based on data reduction is proposed. Data reduction aims at finding patterns or regularities within certain features, allowing to induce the so-called prototypes which should be retained for further use during the learning process. The considered approach assumes that data reduction through instance and feature selection is carried out independently at each site by a team of agents. To assure obtaining homogenous prototypes the feature selection requires coordination. The proposed approach provides such coordination by collaboration of agents. In the process of data reduction heterogeneous prototypes can be subsequently merged to create a compact representation of the distributed data repositories and, next, based on such a compact representation a selected meta-learning technique can be applied for generating the global classifier. The paper proposes and explains strategies for agent collaboration producing a common set of features and strategies for constructing combiner classifier. Suggested strategies are evaluated experimentally and compared. The paper includes a detailed description of the proposed approaches and a discussion of the computational experiment results.