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Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability

Evaluating behavioral patterns through logic mining within a given dataset has become a primary focus in current research. Unfortunately, there are several weaknesses in the research regarding the logic mining models, including an uncertainty of the attribute selected in the model, random distributi...

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Main Authors: Romli, Nurul Atiqah, Zulkepli, Nur Fariha Syaqina, Kasihmuddin, Mohd Shareduwan Mohd, Zamri, Nur Ezlin, Rusdi, Nur 'Afifah, Manoharam, Gaeithry, Mansor, Mohd. Asyraf, Jamaludin, Siti Zulaikha Mohd, Malik, Amierah Abdul
Format: Article
Language:English
Published: American Institute of Mathematical Sciences 2024
Online Access:http://psasir.upm.edu.my/id/eprint/114273/1/114273.pdf
http://psasir.upm.edu.my/id/eprint/114273/
http://www.aimspress.com/article/doi/10.3934/math.20241087
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spelling my.upm.eprints.1142732025-01-13T04:50:16Z http://psasir.upm.edu.my/id/eprint/114273/ Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability Romli, Nurul Atiqah Zulkepli, Nur Fariha Syaqina Kasihmuddin, Mohd Shareduwan Mohd Zamri, Nur Ezlin Rusdi, Nur 'Afifah Manoharam, Gaeithry Mansor, Mohd. Asyraf Jamaludin, Siti Zulaikha Mohd Malik, Amierah Abdul Evaluating behavioral patterns through logic mining within a given dataset has become a primary focus in current research. Unfortunately, there are several weaknesses in the research regarding the logic mining models, including an uncertainty of the attribute selected in the model, random distribution of negative literals in a logical structure, non-optimal computation of the best logic, and the generation of overfitting solutions. Motivated by these limitations, a novel logic mining model incorporating the mechanism to control the negative literal in the systematic Satisfiability, namely Weighted Systematic 2 Satisfiability in Discrete Hopfield Neural Network, is proposed as a logical structure to represent the behavior of the dataset. For the proposed logic mining models, we used ratio of r to control the distribution of the negative literals in the logical structures to prevent overfitting solutions and optimize synaptic weight values. A new computational approach of the best logic by considering both true and false classification values of the learning system was applied in this work to preserve the significant behavior of the dataset. Additionally, unsupervised learning techniques such as Topological Data Analysis were proposed to ensure the reliability of the selected attributes in the model. The comparative experiments of the logic mining models by utilizing 20 repository real-life datasets were conducted from repositories to assess their efficiency. Following the results, the proposed logic mining model dominated in all the metrics for the average rank. The average ranks for each metric were Accuracy (7.95), Sensitivity (7.55), Specificity (7.93), Negative Predictive Value (7.50), and Mathews Correlation Coefficient (7.85). Numerical results and in-depth analysis demonstrated that the proposed logic mining model consistently produced optimal induced logic that best represented the real-life dataset for all the performance metrics used in this study. American Institute of Mathematical Sciences 2024-07-17 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/114273/1/114273.pdf Romli, Nurul Atiqah and Zulkepli, Nur Fariha Syaqina and Kasihmuddin, Mohd Shareduwan Mohd and Zamri, Nur Ezlin and Rusdi, Nur 'Afifah and Manoharam, Gaeithry and Mansor, Mohd. Asyraf and Jamaludin, Siti Zulaikha Mohd and Malik, Amierah Abdul (2024) Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability. Aims Mathematics, 9 (8). pp. 22321-22365. ISSN 2473-6988; eISSN: 2473-6988 http://www.aimspress.com/article/doi/10.3934/math.20241087 10.3934/math.20241087
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Evaluating behavioral patterns through logic mining within a given dataset has become a primary focus in current research. Unfortunately, there are several weaknesses in the research regarding the logic mining models, including an uncertainty of the attribute selected in the model, random distribution of negative literals in a logical structure, non-optimal computation of the best logic, and the generation of overfitting solutions. Motivated by these limitations, a novel logic mining model incorporating the mechanism to control the negative literal in the systematic Satisfiability, namely Weighted Systematic 2 Satisfiability in Discrete Hopfield Neural Network, is proposed as a logical structure to represent the behavior of the dataset. For the proposed logic mining models, we used ratio of r to control the distribution of the negative literals in the logical structures to prevent overfitting solutions and optimize synaptic weight values. A new computational approach of the best logic by considering both true and false classification values of the learning system was applied in this work to preserve the significant behavior of the dataset. Additionally, unsupervised learning techniques such as Topological Data Analysis were proposed to ensure the reliability of the selected attributes in the model. The comparative experiments of the logic mining models by utilizing 20 repository real-life datasets were conducted from repositories to assess their efficiency. Following the results, the proposed logic mining model dominated in all the metrics for the average rank. The average ranks for each metric were Accuracy (7.95), Sensitivity (7.55), Specificity (7.93), Negative Predictive Value (7.50), and Mathews Correlation Coefficient (7.85). Numerical results and in-depth analysis demonstrated that the proposed logic mining model consistently produced optimal induced logic that best represented the real-life dataset for all the performance metrics used in this study.
format Article
author Romli, Nurul Atiqah
Zulkepli, Nur Fariha Syaqina
Kasihmuddin, Mohd Shareduwan Mohd
Zamri, Nur Ezlin
Rusdi, Nur 'Afifah
Manoharam, Gaeithry
Mansor, Mohd. Asyraf
Jamaludin, Siti Zulaikha Mohd
Malik, Amierah Abdul
spellingShingle Romli, Nurul Atiqah
Zulkepli, Nur Fariha Syaqina
Kasihmuddin, Mohd Shareduwan Mohd
Zamri, Nur Ezlin
Rusdi, Nur 'Afifah
Manoharam, Gaeithry
Mansor, Mohd. Asyraf
Jamaludin, Siti Zulaikha Mohd
Malik, Amierah Abdul
Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability
author_facet Romli, Nurul Atiqah
Zulkepli, Nur Fariha Syaqina
Kasihmuddin, Mohd Shareduwan Mohd
Zamri, Nur Ezlin
Rusdi, Nur 'Afifah
Manoharam, Gaeithry
Mansor, Mohd. Asyraf
Jamaludin, Siti Zulaikha Mohd
Malik, Amierah Abdul
author_sort Romli, Nurul Atiqah
title Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability
title_short Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability
title_full Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability
title_fullStr Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability
title_full_unstemmed Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability
title_sort unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete hopfield neural networks via weighted systematic 2 satisfiability
publisher American Institute of Mathematical Sciences
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/114273/1/114273.pdf
http://psasir.upm.edu.my/id/eprint/114273/
http://www.aimspress.com/article/doi/10.3934/math.20241087
_version_ 1823093098418274304
score 13.244413

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