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. © Springer-Verlag Berlin Heidelberg 2011

Castelli, M., Manzoni, L., Vanneschi, L. (2011). Multi objective genetic programming for feature construction in classification problems. In Lecture Notes in Computer Science (pp.503-506) [10.1007/978-3-642-25566-3_39].

Multi objective genetic programming for feature construction in classification problems

CASTELLI, MAURO;MANZONI, LUCA;VANNESCHI, LEONARDO
2011

Abstract

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. © Springer-Verlag Berlin Heidelberg 2011
No
paper
Computer Science (all); Theoretical Computer Science
English
5 International Conference on Learning and Intelligent Optimization, LION 2011
978-3-642-25566-3
Castelli, M., Manzoni, L., Vanneschi, L. (2011). Multi objective genetic programming for feature construction in classification problems. In Lecture Notes in Computer Science (pp.503-506) [10.1007/978-3-642-25566-3_39].
Castelli, M; Manzoni, L; Vanneschi, L
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/60772
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