McDiarmid Drift Detection Methods for Evolving Data Streams

TL;DR

The paper introduces MDDM, a novel concept drift detection method using McDiarmid's inequality, which effectively detects changes in evolving data streams with shorter delays and fewer false negatives.

Contribution

It presents the McDiarmid Drift Detection Method (MDDM), a new approach that improves early detection of concept drift in data streams by emphasizing recent data and leveraging McDiarmid's inequality.

Findings

MDDM outperforms existing methods in detection delay.

MDDM achieves lower false negative rates.

MDDM maintains high classification accuracy.

Abstract

Increasingly, Internet of Things (IoT) domains, such as sensor networks, smart cities, and social networks, generate vast amounts of data. Such data are not only unbounded and rapidly evolving. Rather, the content thereof dynamically evolves over time, often in unforeseen ways. These variations are due to so-called concept drifts, caused by changes in the underlying data generation mechanisms. In a classification setting, concept drift causes the previously learned models to become inaccurate, unsafe and even unusable. Accordingly, concept drifts need to be detected, and handled, as soon as possible. In medical applications and emergency response settings, for example, change in behaviours should be detected in near real-time, to avoid potential loss of life. To this end, we introduce the McDiarmid Drift Detection Method (MDDM), which utilizes McDiarmid's inequality in order to detect concept drift. The MDDM approach proceeds by sliding a window over prediction results, and associate window entries with weights. Higher weights are assigned to the most recent entries, in order to emphasize their importance. As instances are processed, the detection algorithm compares a weighted mean of elements inside the sliding window with the maximum weighted mean observed so far. A significant difference between the two weighted means, upper-bounded by the McDiarmid inequality, implies a concept drift. Our extensive experimentation against synthetic and real-world data streams show that our novel method outperforms the state-of-the-art. Specifically, MDDM yields shorter detection delays as well as lower false negative rates, while maintaining high classification accuracies.