Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

TreeWorks: Advances in Scalable Decision Trees

Harper, Paul Robert and Leite Jr., Evandro 2008. TreeWorks: Advances in Scalable Decision Trees. International Journal of Healthcare Information Systems and Informatics (IJHISI) 3 (4) , pp. 53-68. 10.4018/jhisi.2008100104

Full text not available from this repository.

Abstract

Decision trees are hierarchical, sequential classification structures that recursively partition the set of observations (data) and are used to represent rules underlying the observations. This article describes the development of TreeWorks, a tool that enhances existing decision tree theory and overcomes some of the common limitations such as scalability and the ability to handle large databases. We present a heuristic that allows TreeWorks to cope with observation sets that contain several distinct values of categorical data, as well as the ability to handle very large datasets by overcoming issues with computer main memory. Furthermore, our tool incorporates a number of useful features such as the ability to move data across terminal nodes, allowing for the construction of trees combining statistical accuracy with expert opinion. Finally, we discuss ways that decision trees can be combined with Operational Research health care models, for more effective and efficient planning and management of health care processes.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Publisher: IGI Global
ISSN: 1555-3396
Last Modified: 04 Jun 2017 02:47
URI: http://orca.cf.ac.uk/id/eprint/12148

Citation Data

Cited 4 times in Google Scholar. View in Google Scholar

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item