Multi objective genetic programming for feature construction in classication problems

Research output: Chapter in Book/Report/Conference proceedingChapter


This work introduces a new technique for features construction in classification problems by means of multi objective genetic programming (MOGP). The final goal is to improve the generalization ability of the final classifier. MOGP can help in finding solutions with a better generalization ability with respect to standard genetic programming as stated in [1]. The main issue is the choice of the criteria that must be optimized by MOGP. In this work the construction of new features is guided by two criteria: the first one is the entropy of the target classes as in [7] while the second is inspired by the concept of margin used in support vector machines.
Original languageUnknown
Title of host publicationLearning and Intelligent Optimization
EditorsA. Coello E C.
Place of PublicationBerlin
ISBN (Print)978-3-642-25565-6
Publication statusPublished - 1 Jan 2011

Publication series

NameLecture Notes in Computer Science

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