Multi Objective Genetic Programming for Feature Construction in Classification Problems
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 . 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  while the second is inspired by the concept of margin used in support vector machines.
KeywordsSupport Vector Machine Genetic Programming Target Class Feature Construction Good Generalization Ability
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