Regression Trees with Genetic Programming

• Main Authors: Skywood Chen, 陳顯旭
• Other Authors: 邱昭彰
• Format: Others
• Language: en_US
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/69516334371377005962

碩士 === 元智大學 === 資訊管理研究所 === 88 === Data mining is the automated search for interesting and useful relationships between attributes in database. In many of the best techniques (such as neural networks) yield little in terms of usable rules. In recent years, there has been considerable success in the use of Genetic Programming (GP) to evolve pattern recognizers. In this article we presents a GP-regression tree (GPRT) algorithm as an alternative to existing regression tree approaches. We focus on using genetic programming and local linear regression to construct regression trees by genetic selection of features and split points, then using tree pruning and evaluation trying to find out an optimized regression tree. Experimental results for two data mining regression problems are presented and compared with other regression algorithms. Experiments show our approach has a number of advantages over existing regression algorithms. A prototype was presented to assistant the regression tree construction and produced a set of rules to support decision makers.