Blind Multiclass Ensemble Classification

TL;DR

This paper introduces a blind ensemble learning method that combines multiple classifiers without prior knowledge of their training labels, using tensor and matrix factorization techniques to improve classification performance.

Contribution

It presents a novel blind ensemble classification scheme based on moment matching and tensor factorization, with theoretical analysis and empirical evaluation.

Findings

The method effectively combines classifiers without label knowledge.

Performance analysis confirms the scheme's robustness.

Experimental results demonstrate improved accuracy.

Abstract

The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the "best" performing one, for a given dataset. Ensemble learning aims at such high-performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning from ensembles of classifiers, using a moment matching method that leverages joint tensor and matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. A rigorous performance analysis is derived and the proposed scheme is evaluated on synthetic and real datasets.