[{"id":1163,"title":"Synthetic Dataset Generation for Concept Drift Adaptation","sha":"9bbaa226b588ed5949a658feeb282467","user_id":null,"state":"published","body":"Concept drift, a phenomenon where the statistical properties of the target variable change over time, poses a significant challenge in data stream mining. The low amount of real word datasets with concept drift make this challenge harder on many researchers. This brief proposes an approach to generate synthetic datasets that incorporate concept drift, aiding in the training and testing of machine learning models for detecting and adapting to such drifts. The process involves defining the concept drift, generating synthetic data reflecting this drift, splitting the data into training and testing sets, and iteratively training, testing, and improving the model based on its performance. This approach aims to enhance the model’s adaptability to concept drift, thereby improving its predictive accuracy over time.","created_at":"2023-10-01T22:10:45.497Z","updated_at":"2023-11-16T08:11:43.267Z","zenodo_id":8423520,"doi":"https://doi.org/10.5281/zenodo.8423520","subject":null,"tags":["concept drift","incremental learning","data minning","machine learning","online learning"],"vote_count":1,"view_count":1,"score":1.1,"deleted":false,"muted":false,"tweeted":false,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]