Interactive Multiagent Adaptation of Individual Classification Models for Decision Support

An essential prerequisite for informed decision-making of intelligent agents is direct access to empirical knowledge for situation assessment. This contribution introduces an agent-oriented knowledge management framework for learning agents facing impediments in self-contained acquisition of classification models. The framework enables the emergence of dynamic knowledge networks among benevolent agents forming a community of practice in open multiagent systems. Agents in an advisee role are enabled to pinpoint learning impediments in terms of critical training cases and to engage in a goal-directed discourse with an advisor panel to overcome identified issues. The advisors provide arguments supporting and hence explaining those critical cases. Using such input as additional background knowledge, advisees can adapt their models in iterative relearning organized as a search through model space. An extensive empirical evaluation in two real-world domains validates the presented approach

Variable precision rough set theory decision support system: With an application to bank rating prediction

This dissertation considers, the Variable Precision Rough Sets (VPRS) model, and its development within a comprehensive software package (decision support system), incorporating methods of re sampling and classifier aggregation. The concept of /-reduct aggregation is introduced, as a novel approach to classifier aggregation within the VPRS framework. The software is applied to the credit rating prediction problem, in particularly, a full exposition of the prediction and classification of Fitch's Individual Bank Strength Ratings (FIBRs), to a number of banks from around the world is presented. The ethos of the developed software was to rely heavily on a simple 'point and click' interface, designed to make a VPRS analysis accessible to an analyst, who is not necessarily an expert in the field of VPRS or decision rule based systems. The development of the software has also benefited from consultations with managers from one of Europe's leading hedge funds, who gave valuable insight, advice and recommendations on what they considered as pertinent issues with regards to data mining, and what they would like to see from a modern data mining system. The elements within the developed software reflect each stage of the knowledge discovery process, namely, pre-processing, feature selection, data mining, interpretation and evaluation. The developed software encompasses three software packages, a pre-processing package incorporating some of the latest pre-processing and feature selection methods a VPRS data mining package, based on a novel "vein graph" interface, which presents the analyst with selectable /-reducts over the domain of / and a third more advanced VPRS data mining package, which essentially automates the vein graph interface for incorporation into a re-sampling environment, and also implements the introduced aggregated /-reduct, developed to optimise and stabilise the predictive accuracy of a set of decision rules induced from the aggregated /-reduct