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
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
Selecting representative examples and attributes by a genetic algorithm
Intelligent Data Analysis
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
Cluster-based instance selection for machine classification
Knowledge and Information Systems
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
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
| Hi-index | 0.00 |
The problem addressed in this paper concerns data reduction through instance selection. The paper proposes an approach based on instance selection from clusters. The process of selection and learning is executed by a team of agents. The approach aims at obtaining a compact representation of the dataset, where the upper bound on the size of data is determined by the user. The basic assumption is that the instance selection is carried out after the training data have been grouped into clusters. The cluster initialization and integration strategies are proposed and experimentally evaluated.