International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural networks and open texture
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Experiment in Discovering Association Rules in the Legal Domain
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Induction of defeasible logic theories in the legal domain
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Obtaining Best Parameter Values for Accurate Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Formal systems for persuasion dialogue
The Knowledge Engineering Review
Argument based machine learning applied to law
Artificial Intelligence and Law - Argumentation in artificial intelligence and law
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
Arguments from Experience: The PADUA Protocol
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
Identifying and eliminating mislabeled training instances
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
| Hi-index | 0.01 |
A process, based on argumentation theory, is described for classifying very noisy data. More specifically a process founded on a concept called "arguing from experience" is described where by several software agents "argue" about the classification of a new example given individual "case bases" containing previously classified examples. Two "arguing from experience" protocols are described: PADUA which has been applied to binary classification problems and PISA which has been applied to multi-class problems. Evaluation of both PADUA and PISA indicates that they operate with equal effectiveness to other classification systems in the absence of noise. However, the systems out-perform comparable systems given very noisy data. Keywords: Classification, Argumentation, Noisy data.