ENSEMBLE CLASSIFIER FOR IMPUTATION OF MOBILITY DATA OF UNKNOWN SUBJECT - GHOSE, AVIK; CHATTERJEE, ARNAB; KUMARI, SHASHEE; BHATTACHARYA, SAKYAJIT (2024)
Research work in the literature on imputation of mobility data for missing records of a subject's location trajectory has been specifically revolved around usage of historical data. Thus, performances drop when missing records or imputation mobility data for unknown subject with very little or no historical data has to be predicted. A method and system for training an ensemble classifier for imputation of mobility data of unknown subject based on cohort of the unknown subject is disclosed. The method and system disclosed herein exploits the knowledge that semantic trajectories of different individuals has considerable similarity when individuals belong to the same cohort. This concept is used by the method to predict the behavior of all the individuals in a cohort using ensemble classifier, also referred to as imputation model, trained on the semantic location data of a fraction of total individuals in the cohort with a certain accuracy.