ICM Ensemble with Novel Betting Functions for Concept Drift

TL;DR

This paper introduces an improved ICM ensemble method with novel betting functions and multiple density estimators for more effective concept drift detection, demonstrating superior performance on benchmark datasets.

Contribution

The study presents a refined ICM ensemble approach incorporating multiple density estimators and novel betting functions, advancing concept drift detection capabilities.

Findings

Outperforms previous ICM methods in accuracy.

Matches or exceeds state-of-the-art techniques.

Ensemble size impacts prediction accuracy and availability.

Abstract

This study builds upon our previous work by introducing a refined Inductive Conformal Martingale (ICM) approach for addressing Concept Drift (CD). Specifically, we enhance our previously proposed CAUTIOUS betting function to incorporate multiple density estimators for improving detection ability. We also combine this betting function with two base estimators that have not been previously utilized within the ICM framework: the Interpolated Histogram and Nearest Neighbor Density Estimators. We assess these extensions using both a single ICM and an ensemble of ICMs. For the latter, we conduct a comprehensive experimental investigation into the influence of the ensemble size on prediction accuracy and the number of available predictions. Our experimental results on four benchmark datasets demonstrate that the proposed approach surpasses our previous methodology in terms of performance while matching or in many cases exceeding that of three contemporary state-of-the-art techniques.