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
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
Teaching case-based argumentation through a model and examples
Teaching case-based argumentation through a model and examples
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Predicting outcomes of case based legal arguments
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
AGATHA: using heuristic search to automate the construction of case law theories
Artificial Intelligence and Law - Argumentation in artificial intelligence and law
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
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Automatically classifying case texts and predicting outcomes
Artificial Intelligence and Law
Multi-party argument from experience
ArgMAS'09 Proceedings of the 6th international conference on Argumentation in Multi-Agent Systems
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Error rates in the assessment of routine claims for welfare benefits have been found to be very high in Netherlands, USA and UK. This is a significant problem both in terms of quality of service and financial loss through over payments. These errors also present challenges for machine learning programs using the data. In this paper we propose a way of addressing this problem by using a process of moderation, in which agents argue about the classification on the basis of data from distinct groups of assessors. Our agents employ an argument based dialogue protocol (PADUA) in which the agents produce arguments directly from a database of cases, with each agent having their own separate database. We describe the protocol and report encouraging results from a series of experiments comparing PADUA with other classifiers, and assessing the effectiveness of the moderation process.