DocumentCode :
2217922
Title :
A Memetic Genetic Programming with decision tree-based local search for classification problems
Author :
Wang, Pu ; Tang, Ke ; Tsang, Edward P K ; Yao, Xin
Author_Institution :
Sch. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China
fYear :
2011
fDate :
5-8 June 2011
Firstpage :
917
Lastpage :
924
Abstract :
In this work, we propose a new genetic programming algorithm with local search strategies, named Memetic Genetic Programming(MGP), for classification problems. MGP aims to acquire a classifier with large Area Under the ROC Curve (AUC), which has been proved to be a better performance metric for traditionally used metrics (e.g., classification accuracy). Three new points are presented in our new algorithm. First, a new representation called statistical genetic decision tree (SGDT) for GP is proposed on the basis of Genetic Decision Tree (GDT). Second, a new fitness function is designed by using statistic in formation from SGDT. Third, the concept of memetic computing is introduced into SGDT. As a result, the MGP is equipped with a local search method based on the training algorithms for decision trees. The efficacy of the MGP is empirically justified against a number of relevant approaches.
Keywords :
decision trees; genetic algorithms; learning (artificial intelligence); pattern classification; search problems; area under ROC curve; classification problems; classifier; decision tree-based local search; fitness function; memetic computing; memetic genetic programming; statistical genetic decision tree; training algorithms; Accuracy; Algorithm design and analysis; Decision trees; Genetics; Measurement; Memetics; Training data; AUC; Classification; Genetic Programming; Memetic Algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location :
New Orleans, LA
ISSN :
Pending
Print_ISBN :
978-1-4244-7834-7
Type :
conf
DOI :
10.1109/CEC.2011.5949716
Filename :
5949716
Link To Document :
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