Genetic Programming with Random Binary Decomposition for Multi-Class Classification Problems
Lushen Liao, Adam Kotaro Pindur, Hitoshi Iba · 2021
This paper introduces a new Genetic Programming (GP) based classification framework for multiclass classification problems. The proposed framework uses a binary decomposition-based GP method to extract new features to enhance the performance of classifiers in the multiclass classification task. We firstly introduce a random binary decomposition method that uses a part-vs-part strategy to decompose the multiclass problems which increase the number of binary problems that can be decomposed from a multiclass problem. Then the details of combining GP with this binary decomposition method for feature extraction are explained. Finally, we compare our method to several popular ML methods and traditional GP methods in a broad set of benchmark problems. The outcome shows the performance of classifiers is enhanced for multi-class classification tasks when combined with this technique. The effect of applying this framework to different classifiers and large real-world data set is also explored. The results suggest the effectiveness and universality of our method.