# Blind Multiclass Ensemble Classification

arXiv: https://arxiv.org/abs/1712.02903 · HTML: https://mlsanity.com/paper/1712.02903/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2017.

## Built on by (most cited first, 8 of 8)

- [Identifiability of Label Noise Transition Matrix](https://mlsanity.com/paper/2202.02016/) (2022)
- [DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples](https://mlsanity.com/paper/2110.13740/) (2021)
- [Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization](https://mlsanity.com/paper/2306.03288/) (2023)
- [Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization](https://mlsanity.com/paper/2106.07193/) (2021)
- [Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective](https://mlsanity.com/paper/2407.06902/) (2024)
- [Detecting adversaries in Crowdsourcing](https://mlsanity.com/paper/2110.04117/) (2021)
- [Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach](https://mlsanity.com/paper/2301.01405/) (2023)
- [Ranking and Combining Latent Structured Predictive Scores without Labeled Data](https://mlsanity.com/paper/2408.07796/) (2024)

## Abstract (arXiv)

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.