From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors

03/21/2022
by   Firas Bayram, et al.
0

The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system's life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model's performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model's performance in the predictive system. The paper concludes by discussing open research challenges and possible research directions.

READ FULL TEXT

page 7

page 10

page 29

research
03/09/2022

Autoregressive based Drift Detection Method

In the classic machine learning framework, models are trained on histori...
research
11/12/2015

Characterizing Concept Drift

Most machine learning models are static, but the world is dynamic, and i...
research
06/15/2022

Learn to Adapt: Robust Drift Detection in Security Domain

Deploying robust machine learning models has to account for concept drif...
research
06/24/2020

Ensuring Learning Guarantees on Concept Drift Detection with Statistical Learning Theory

Concept Drift (CD) detection intends to continuously identify changes in...
research
06/21/2021

Graceful Degradation and Related Fields

When machine learning models encounter data which is out of the distribu...
research
07/27/2022

Detecting Concept Drift in the Presence of Sparsity – A Case Study of Automated Change Risk Assessment System

Missing values, widely called as sparsity in literature, is a common cha...
research
05/06/2023

Detecting Concept Drift for the reliability prediction of Software Defects using Instance Interpretation

In the context of Just-In-Time Software Defect Prediction (JIT-SDP), Con...

Please sign up or login with your details

Forgot password? Click here to reset