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    A hybrid model combining neural networks and decision tree for comprehension detection

    Crockett, KA ORCID logoORCID: https://orcid.org/0000-0003-1941-6201, O'Shea, James ORCID logoORCID: https://orcid.org/0000-0001-5645-2370, Khan, Wasiq ORCID logoORCID: https://orcid.org/0000-0002-7511-3873 and Bandar, Zuhair (2018) A hybrid model combining neural networks and decision tree for comprehension detection. In: 2018 International Joint Conference on Neural Networks (IJCNN), 08 July 2018 - 13 July 2018, Rio de Janeiro, Brazil.


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    The Artificial Neural Network is generally considered to be an effective classifier, but also a “Black Box” component whose internal behavior cannot be understood by human users. This lack of transparency forms a barrier to acceptance in high-stakes applications by the general public. This paper investigates the use of a hybrid model comprising multiple artificial neural networks with a final C4.5 decision tree classifier to investigate the potential of explaining the classification decision through production rules. Two large datasets collected from comprehension studies are used to investigate the value of the C4.5 decision tree as the overall comprehension classifier in terms of accuracy and decision transparency. Empirical trials show that higher accuracies are achieved through using a decision tree classifier, but the significant tree size questions the rule transparency to a human.

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