# Item Response Theory based Ensemble in Machine Learning

arXiv: https://arxiv.org/abs/1911.04616 · HTML: https://mlsanity.com/paper/1911.04616/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://mlsanity.com/paper/2308.00247/) (2023)
- [Scalable Learning of Item Response Theory Models](https://mlsanity.com/paper/2403.00680/) (2024)
- [Unsupervised Anomaly Detection Ensembles using Item Response Theory](https://mlsanity.com/paper/2106.06243/) (2021)
- [Optimal and Private Learning from Human Response Data](https://mlsanity.com/paper/2303.06234/) (2023)
- [Evidence-Unit Fairness and the Limits of Query-Adaptive Sparse-Dense Fusion in Financial Document Retrieval](https://mlsanity.com/paper/2608.00183/) (2026)
- [On Evaluation of Vision Datasets and Models using Human Competency Frameworks](https://mlsanity.com/paper/2409.04041/) (2024)

## Abstract (arXiv)

In this article, we propose a novel probabilistic framework to improve the accuracy of a weighted majority voting algorithm. In order to assign higher weights to the classifiers which can correctly classify hard-to-classify instances, we introduce the Item Response Theory (IRT) framework to evaluate the samples' difficulty and classifiers' ability simultaneously. Three models are created with different assumptions suitable for different cases. When making an inference, we keep a balance between the accuracy and complexity. In our experiment, all the base models are constructed by single trees via bootstrap. To explain the models, we illustrate how the IRT ensemble model constructs the classifying boundary. We also compare their performance with other widely used methods and show that our model performs well on 19 datasets.