Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network

Gaeithry Manoharam, Mohd Shareduwan Mohd Kasihmuddin (), Siti Noor Farwina Mohamad Anwar Antony, Nurul Atiqah Romli, Nur ‘Afifah Rusdi, Suad Abdeen and Mohd. Asyraf Mansor
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Gaeithry Manoharam: School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
Mohd Shareduwan Mohd Kasihmuddin: School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
Siti Noor Farwina Mohamad Anwar Antony: School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
Nurul Atiqah Romli: School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
Nur ‘Afifah Rusdi: School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
Suad Abdeen: School of Mathematical Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
Mohd. Asyraf Mansor: School of Distance Education, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia

Mathematics, 2023, vol. 11, issue 9, 1-30

Abstract: Choosing the best attribute from a dataset is a crucial step in effective logic mining since it has the greatest impact on improving the performance of the induced logic. This can be achieved by removing any irrelevant attributes that could become a logical rule. Numerous strategies are available in the literature to address this issue. However, these approaches only consider low-order logical rules, which limit the logical connection in the clause. Even though some methods produce excellent performance metrics, incorporating optimal higher-order logical rules into logic mining is challenging due to the large number of attributes involved. Furthermore, suboptimal logical rules are trained on an ineffective discrete Hopfield neural network, which leads to suboptimal induced logic. In this paper, we propose higher-order logic mining incorporating a log-linear analysis during the pre-processing phase, the multi-unit 3-satisfiability-based reverse analysis with a log-linear approach. The proposed logic mining also integrates a multi-unit discrete Hopfield neural network to ensure that each 3-satisfiability logic is learned separately. In this context, our proposed logic mining employs three unique optimization layers to improve the final induced logic. Extensive experiments are conducted on 15 real-life datasets from various fields of study. The experimental results demonstrated that our proposed logic mining method outperforms state-of-the-art methods in terms of widely used performance metrics.

Keywords: logic mining; data mining; log-linear analysis; reverse analysis; statistical classification; evolutionary computation; discrete Hopfield neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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