C4.5: programs for machine learning
C4.5: programs for machine learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Techniques for Estimating the Computation and Communication Costs of Distributed Data Mining
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
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
Knowledge and Information Systems
Scalable Representative Instance Selection and Ranking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
The COMPSET algorithm for subset selection
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
Agent-based distributed data mining: the KDEC scheme
Intelligent information agents
Multi-sorting algorithm for finding pairs of similar short substrings from large-scale string data
Knowledge and Information Systems - Special Issue:Best Papers from the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2008);Guest Editors: Takashi Washio, Einoshin Suzuki and Kai Ming Ting
Cellular GEP-induced classifiers
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Cluster integration for the cluster-based instance selection
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Distributed learning with data reduction
Transactions on computational collective intelligence IV
A new cluster-based instance selection algorithm
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
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The aim of the paper is to propose and evaluate an agent-based population learning algorithm generating, by the prototype selection, a representative training dataset of the required size. It is assumed that prototypes are selected from clusters. The process of selection is executed by a team of agents, which execute various local search procedures and cooperate to find-out a solution to the considered problem. Rules for agent cooperation are defined within working strategies. In this paper influence of two different strategies and the population size on performance of the algorithm is investigated.