This paper proposes an approach to solving node weightings in a tree structure. The tree represents expertise used to quantify risks associated with mental-health problems and it is incorporated within a web-based decision support system called GRiST. The aim of the algorithm is to find the set of relative node weightings in the tree that helps GRiST simulate the clinical risk judgements given by mental-health experts. The results show that a very large number of nodes (several thousand for GRiST) can have their weights calculated from the clinical judgements associated with a few hundred cases (200 for GRiST). This greatly reduces the experts' elicitation tasks by ensuring they do not need to provide their own estimation of node weights throughout the tree. The approach has the potential for reducing elicitation load in similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.
|Title of host publication||Proc. - The 3rd Int. Multi-Conf. Computing in the Global Information Technology, ICCGI 2008 in Conjunction with ComP2P 2008: The 1st Int. Workshop on Computational P2P Networks: Theory and Practice|
|Number of pages||6|
|Publication status||Published - 26 Sept 2008|
|Event||3rd International Multi-Conference on Computing in the Global Information Technology, ICCGI 2008 - Athens, United Kingdom|
Duration: 27 Jul 2008 → 1 Aug 2008
|Conference||3rd International Multi-Conference on Computing in the Global Information Technology, ICCGI 2008|
|Period||27/07/08 → 1/08/08|