Classification of Melanomas in situ using Knowledge Discovery with Explained CBR
Publication Type:Journal Article
Source:Artificial Intelligence in Medicine, Volume 51, p.12 (2011)
Keywords:knowledge discovery; classification; clustering; CBR; application
The goal of Knowledge Discovery is to extract knowledge from a set of data. Most common techniques used in knowledge discovery are based on clustering methods whose goal is to analyze a set of objects and to obtain clusters based on the similarity between these objects. A desirable characteristic of clustering results is that they should be easily understandable by domain experts. In this paper we introduce LazyCL, a procedure using a lazy learning method to produce explanations on clusters of unlabeled cases. These explanations are the basis on which experts can perform knowledge discovery. Here we use LazyCL to generate a domain theory for classification of melanomas in situ.
DipTools: Experimental Data Visualization Tool for the DipGame Testbed (Demonstration)
A Testbed for Multiagent Systems
Source:IIIA-TR-2009-09, IIIA-CSIC, Bellaterra, Barcelona, p.18 (2009)
Keywords:application; testbed; diplomacy game
There is a chronic lack of shared application domains to test the research models and agent architectures on areas like negotiation, argumentation, trust and reputation. In this paper we introduce such a friendly testbed that we used for all such purposes. The testbed is based on the Diplomacy Game due to its lack of random moves and because of the essential role that negotiation and the relationships between the players play in the game. The testbed may also profit from the existence of a community of bot developers and a large number of human players that would provide data for our experiments. We offer the infrastructure and make it freely available to the MAS community.