Hybrid Resampling for Imbalanced Class Handling on Web Phishing Classification Dataset
Yoga Pristyanto, Akhmad Dahlan · 2019
From the previous work related to web phishing, the researchers overlook the imbalanced class problem on the dataset. theoretically, the majority of classification methods would assume that the nature of the class distribution is balanced. It caused the classification's performance of the method will be declining. Therefore, the mechanism of imbalanced class handling is severely needed. In our study, One Sided-Selection and Synthetic Minority Over-Sampling Technique are used to handle the imbalanced class condition. Those algorithms work to balancing the class distribution of the dataset so that the accuracy and the g-mean score of the classification will be enhanced. Based on the result, the combination of those methods (OSS and SMOTE) can enhance the classification's result significantly either on binary type class and multiclass type dataset. Hence, the combination of OSS and SMOTE can be a plausible option to handle the imbalanced class problem on the web phishing classification either on binary class and multiclass datasets.