Item Response Theory based Ensemble in Machine Learning
This is an incremental improvement for ensemble learning methods in machine learning.
The authors tackled the problem of improving weighted majority voting accuracy by introducing an Item Response Theory (IRT) framework to assign higher weights to classifiers that correctly classify hard-to-classify instances, resulting in a model that performs well on 19 datasets.
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.