A novel stylistic classification method and its experimental study
Suhang Gu, Shitong Wang, Guanjin Wang · Developments of Artificial Intelligence Technologies in Computation and Robotics · 2020
By simulating humanlike stylistic classification behaviors, a novel design methodology called S2CM for stylistic data classification is developed in this study. The core of S2CM is to build a social network consisting of subnetworks corresponding to each data class in the training dataset, and then compute both the influence of each node and the authority of each subnetwork such that style information existing in the training dataset can be well expressed according to the philosophy of social networks. With the built social network, the prediction of S2CM for an unseen sample can be cheaply implemented. Experimental results on artificial and benchmarking datasets show that S2CM outperforms the comparison methods on stylistic data.