A Novel Online Stacked Ensemble for Multi-Label Stream Classification

TL;DR

This paper introduces GOOWE-ML, an online stacked ensemble method for multi-label stream classification that adaptively weights classifiers using spatial modeling, demonstrating superior performance over existing models on real-world datasets.

Contribution

The paper presents a novel online, dynamically-weighted stacked ensemble called GOOWE-ML for multi-label data streams, adaptable with any incremental classifier and showing improved accuracy.

Findings

GOOWE-ML outperforms baseline models on most datasets.

Ensembles with GOOWE-ML achieve higher predictive accuracy.

Spatial modeling effectively assigns optimal classifier weights.

Abstract

As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods have been proposed to tackle this problem, including but not limited to ensemble-based methods. Some of these ensemble-based methods are specifically designed to work with certain multi-label base classifiers; some others employ online bagging schemes to build their ensembles. In this study, we introduce a novel online and dynamically-weighted stacked ensemble for multi-label classification, called GOOWE-ML, that utilizes spatial modeling to assign optimal weights to its component classifiers. Our model can be used with any existing incremental multi-label classification algorithm as its base classifier. We conduct experiments with 4 GOOWE-ML-based multi-label ensembles and 7 baseline models on 7 real-world datasets from diverse areas of interest. Our experiments show that GOOWE-ML ensembles yield consistently better results in terms of predictive performance in almost all of the datasets, with respect to the other prominent ensemble models.