Classification of multiclass datasets using genetic programming
Mahsa Shokri Varniab, Chih‐Cheng Hung, Vahid Khalilzad Sharghi · 2019
This paper1 proposes an approach which uses optimized genetic programming (GP) with a new fitness function for multiclass dataset classification. In place of defining static thresholds as boundaries to differentiate between multiple classes, our work presents a method of classification where a GP system learns the relationships among experiential data and models them mathematically during the evolutionary process. We propose an optimized GP classifier based on a combination of pruning subtrees and a new fitness function. An orthogonal least squares algorithm is also applied in the training phase to create a robust GP classifier. Our approach has been assessed on four multiclass datasets and compared against three existing methods. The analyzed results illustrate that the developed classifier produces a productive and rapid method for classification tasks that outperforms the previous methods for more challenging multiclass classification problems.