Concept drift in streaming data

Author

Tatheer Fatima

Degree

Master of Science in Computer Science

Department

Department of Computer Science

Faculty / School

Faculty of Computer Sciences (FCS)

Date of Submission

2020-06-30

Supervisor

Dr. Tariq Mehmood, Professor, Faculty of Computer Science, Institute of Business Administration (IBA), Karachi

Document type

MSCS Survey Report

Keywords

Concept drift, Machine learning, Streaming data, Unlabeled streaming data

Abstract

World is generating immeasurable amount of data every minute, that needs to be analyzed for better decision making. In order to fulfil this demand of faster analytics, businesses are adopting efficient stream processing and machine learning techniques. However, data streams are particularly challenging to handle. One of the prominent problems faced while dealing with streaming data is concept drift. Concept drift is described as, an unexpected change in the underlying distribution of the streaming data that can be observed as time passes. In this work, we have studied several methods that deal with the problem of concept drift. Most frequently used supervised and unsupervised techniques have been reviewed and we have also surveyed commonly used publicly available artificial and real-world datasets that are used to deal with concept drift issues.

Recommended Citation

Fatima, T. (2020). Concept drift in streaming data (Unpublished MSCS survey report). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/survey-reports-mscs/34