Supervised Learning in the Presence of Concept Drift: A modelling framework

TL;DR

This paper introduces a modeling framework using statistical physics to analyze supervised learning systems like LVQ and neural networks under various concept drift scenarios in non-stationary environments.

Contribution

It develops a mathematical framework for analyzing training dynamics of LVQ and neural networks with different activation functions amid concept drift.

Findings

LVQ algorithms are somewhat suitable for non-stationary environments.

Weight decay does not improve performance under drift.

ReLU networks show different sensitivity to drift compared to sigmoidal networks.

Abstract

We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototype-based Learning Vector Quantization (LVQ) for classification and shallow, layered neural networks for regression tasks. We investigate so-called student teacher scenarios in which the systems are trained from a stream of high-dimensional, labeled data. Properties of the target task are considered to be non-stationary due to drift processes while the training is performed. Different types of concept drift are studied, which affect the density of example inputs only, the target rule itself, or both. By applying methods from statistical physics, we develop a modelling framework for the mathematical analysis of the training dynamics in non-stationary environments. Our results show that standard LVQ algorithms are already suitable for the training in non-stationary environments to a certain extent. However, the application of weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes. Furthermore, we investigate gradient-based training of layered neural networks with sigmoidal activation functions and compare with the use of rectified linear units (ReLU). Our findings show that the sensitivity to concept drift and the effectiveness of weight decay differs significantly between the two types of activation function.