A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis
This is an incremental application of existing methods to a new domain (gender detection in mobile telematics), with no clear problem statement for a specific user group.
The paper tackles gender detection from mobile telematics data by proposing a Choquet fuzzy integral vertical bagging classifier, which outperforms other classifiers in empirical results.
Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.