An Extension of the Gamma Associative Classifier for Dealing With Hybrid Data
Yenny Villuendas-Rey, Cornelio Yáñez-Márquéz, Jarvin A. Antón-Vargas, Itzamá López-Yáñez · IEEE Access · 2019
This paper extends the Gamma associative classifier, making it able to deal with hybrid and incomplete data. In addition, it also encompasses the gamma rough sets model for dealing with such data, introducing the extended gamma rough sets. Some properties of such sets are demonstrated in this paper. In turn, the novel extended gamma rough sets are used to improve the extended gamma associative classifier by selecting the instances. The results indicate that the selection of instances significantly improves the accuracy of the extended gamma associative classifier while reducing its computational cost.