A function-decomposition method for development of hierarchical multi-attribute decision models

A function-decomposition method for development of hierarchical multi-attribute decision models

0.00 Avg rating0 Votes
Article ID: iaor20043207
Country: Netherlands
Volume: 36
Issue: 3
Start Page Number: 215
End Page Number: 233
Publication Date: Jan 2004
Journal: Decision Support Systems
Authors: ,
Abstract:

Function decomposition is a recent machine learning method that develops a hierarchical structure from class-labeled data by discovering new aggregate attributes and their descriptions. Each new aggregate attribute is described by an example set whose complexity is lower than the complexity of the initial set. We show that function decomposition can be used to develop a hierarchical multi-attribute decision model from a given unstructured set of decision examples. The method implemented in a system called HINT is experimentally evaluated on a real-world housing loans allocation problem and on the rediscovery of three hierarchical decision models. The experimentation demonstrates that the decomposition can discover meaningful and transparent decision models of high classification accuracy. We specifically study the effects of human interaction through either assistance or provision of background knowledge for function decomposition, and show that this has a positive effect on both the comprehensibility and classification accuracy.

Reviews

Required fields are marked *. Your email address will not be published.