Ziheng Chen, Hongshik Ahn. Item Response Theory Based Ensemble in Machine Learning. International Journal of Automation and Computing, vol. 17, no. 5, pp.621-636, 2020. https://doi.org/10.1007/s11633-020-1239-y
Citation: Ziheng Chen, Hongshik Ahn. Item Response Theory Based Ensemble in Machine Learning. International Journal of Automation and Computing, vol. 17, no. 5, pp.621-636, 2020. https://doi.org/10.1007/s11633-020-1239-y

Item Response Theory Based Ensemble in Machine Learning

doi: 10.1007/s11633-020-1239-y
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  • Author Bio:

    Ziheng Chen received the B. Sc. degree in statistics from Renmin University of China, China in 2016. He is currently a Ph. D. degree candidate in Department of Applied Mathematics and Statistics, Stony Brook University, USA. His research interests include reinforcement learning, recommending system, tree structure model and ensemble learning theory. E-mail: ziheng.chen@stonybrook.edu (Corresponding author)ORCID iD: 0000-0002-2585-637X

    Hongshik Ahn received the B. Sc. degree in mathematics from Seoul National University, South Korea, and the Ph. D. degree in statistics from University of Wisconsin-Madison, USA in 1992. From 1992 to 1996, he was a mathematical statistician at the National Center for Toxicological Research, U.S. Food and Drug Administration, and a faculty member in the Department of Applied Mathematics and Statistics at Stony Brook University, USA from 1996 to present. He was the first Vice President of SUNY Korea for two years from 2012. Currently, he is a professor at Stony Brook University. He has published 2 books, 3 book chapters, over 70 papers in peer-reviewed journals, and 25 conference papers. His research interests include classification of high-dimensional data, tree-structured regression modeling, survival analysis, and multi-step batch testing for infectious diseases. E-mail: hongshik.ahn@stonybrook.edu

  • Received Date: 2020-02-16
  • Accepted Date: 2020-06-04
  • Publish Online: 2020-09-09
  • Publish Date: 2020-10-01
  • 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. We assigned the weights to classifiers based on their abilities. 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.

     

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