Automatic Learning to Detect Concept Drift

TL;DR

Meta-ADD is a novel machine learning framework that automatically detects and classifies concept drift types in streaming data by tracking error rate patterns and using meta-learning techniques.

Contribution

It introduces a meta-learning approach with prototypical neural networks for automatic concept drift detection and classification, improving understanding and response to data distribution changes.

Findings

Meta-ADD effectively detects various concept drift types.

The framework accurately classifies drift types in streaming data.

Experimental results demonstrate superior performance over existing methods.

Abstract

Many methods have been proposed to detect concept drift, i.e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy of algorithms. However, the most of current detection methods are based on the assessment of the degree of change in the data distribution, cannot identify the type of concept drift. In this paper, we propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework that learns to classify concept drift by tracking the changed pattern of error rates. Specifically, in the training phase, we extract meta-features based on the error rates of various concept drift, after which a meta-detector is developed via a prototypical neural network by representing various concept drift classes as corresponding prototypes. In the detection phase, the learned meta-detector is fine-tuned to adapt to the corresponding data stream via stream-based active learning. Hence, Meta-ADD uses machine learning to learn to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.