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Leveraging Association Rules in Feature Selection for Deep Learning Classification

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Abstract

The number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features affects the accuracy of the classifiers, such as the traditional machine learning classifier. Therefore, in this paper, we proposed the use of association rules to select features from medical data, which results in a dimensionality reduction of the input feature space. The selected features become the input to a deep neural network, particularly ResNet, which is known for its high accuracy of classification results. The conducted experiments prove that the use of association rules to select the most representative features and the use of deep neural networks as a classifier outperformed other traditional machine learning models in terms of the accuracy of classification.

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  1. Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates

    Ruba Kharsa & Zaher Al Aghbari

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  1. Ruba Kharsa
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  2. Zaher Al Aghbari
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Correspondence to Zaher Al Aghbari.

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This article is part of the topical collection “Soft Computing and its Engineering Applications” guest edited by Kanubhai K. Patel, Deepak Garg, Atul Patel and Pawan Lingras.

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Kharsa, R., Al Aghbari, Z. Leveraging Association Rules in Feature Selection for Deep Learning Classification. SN COMPUT. SCI. 5, 112 (2024). https://doi.org/10.1007/s42979-023-02397-6

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