Machine Learning
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Obtaining Best Parameter Values for Accurate Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The class imbalance problem: A systematic study
Intelligent Data Analysis
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
On the Class Imbalance Problem
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
Evaluation Methods for Ordinal Classification
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Arguments from Experience: The PADUA Protocol
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Agent Mining: The Synergy of Agents and Data Mining
IEEE Intelligent Systems
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Improving the performance of the RBF neural networks trained with imbalanced samples
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A comparison of different off-centered entropies to deal with class imbalance for decision trees
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Probabilistic rough set approaches to ordinal classification with monotonicity constraints
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Multi-party argument from experience
ArgMAS'09 Proceedings of the 6th international conference on Argumentation in Multi-Agent Systems
Evaluating the Valuable Rules from Different Experience Using Multiparty Argument Games
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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An approach to multi-agent classification, using an Argumentation from Experience paradigm is describe, whereby individual agents argue for a given example to be classified with a particular label according to their local data. Arguments are expressed in the form of classification rules which are generated dynamically. The advocated argumentation process has been implemented in the PISA multi-agent framework, which is also described. Experiments indicate that the operation of PISA is comparable with other classification approaches and that it can be utilised for Ordinal Classification and Imbalanced Class problems.