Feature Selection for Online Writeprint Identification Using Hybrid Genetic Algorithm

One major task of online writeprint identification is to select the key features for representing the writeprint and facilitating the classifier built by using only the selected feature subset. In this study, we develop a hybrid genetic algorithm: RelieF Fed Genetic Algorithm (RFGA) which incorporates feature weight information produced by using RelieF as the heuristic to identity the key features and improve the identification performance. Experiments are conducted on a test bed encompassing hundreds of reviews posted by 20 Amazon customers to examine the method. The experimental results using RFGA show the proposed approach is effective, obtaining a significant improvement in performance, with satisfactory classification accuracy of 96.67%, and having a heavy reduction in feature dimensionality that is only 3% of the no feature selection baseline.