Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction
DOI:
https://doi.org/10.33448/rsd-v10i13.20830Keywords:
Annotated Paraconsistent Logic; Machine learning; Pattern classification; Mathematical model; Contradiction effect.Abstract
This work describes the development of a computational mathematical model that uses Annotated Paraconsistent Logic - APL and a concept derived from it, the effect of contradiction, to identify patterns in numerical data for pattern classification purposes. The APL admits paraconsistent and paracomplete logical principles, which allow the manipulation of inconsistent and contradictory data, and its use allowed the identification and quantization of the attribute related to the contradiction. To validate the model, series of Raman spectroscopies obtained after exposure of proteins, lipids and nucleic acids, collected from cutaneous tissue cell samples previously examined for the detection of cancerous lesions, identified as basal carcinoma, melanoma and normal, were used. Initially, the attributes related to contradiction, derivative and median obtained from spectroscopies were identified and quantified. A machine learning process with approximately 31.6% of each type of samples detects a sequence of spectroscopies capable of characterizing and classifying the type of lesion through the chosen attributes. Approximately 68.4% of the samples are used for classification tests. The proposed model identified a segment of spectroscopies where the classification of test samples had a hit rate of 76.92%. As a differential and innovation of this work, the use of APL principles in a complete process of training, learning and classification of patterns for numerical data sets stands out, with flexibility to choose the attributes used for the characterization of patterns, and a quantity of samples of about one third of the total required for characterization.
References
Baranauskas, J. A. & Monard, M. C. (2000). “Reviewing some Machine Learning Concepts and Methods”. Relatório Técnico 102, Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos. ftp://ftp.icmc.usp.br/pub/BIBLIOTECA/rel_tec/RT_102.ps.zip.
Carnielli, W. & Coniglio, M. (2018). “Paraconsistent Logic: consistency, contradiction and negation. Logic, epistemology, and the unity of science series”. Springer – Book Review. 42, 2. https://doi.org/10.1590/0100-6045.2018.V41N2.HV.
Carvalho Jr., A., Da Silva Filho, J. I., Mario, M.C., Blos, M. F. & Cruz, C. M. (2018). ”A Study of Paraconsistent Artificial Neural Cell of Learning Applied as PAL2v Filter”. IEEE Latin America Transactions. 16, 202-209, Issue 1, ISSN 1548-0992.
Coelho, M. S., Da Silva Filho, J. I., Cortes, H. M., Carvalho Jr., A., Blos, M. F., Mario, M.C. & Rocco, A. (2019). “Hybrid PI controller constructed with Paraconsistent Annoted Logic”. Control Engineering Practice. 84, 112-124,
Da Costa, N. C. A., Subrahmanian, V. S. & Vago, C. (1991). ”The Paraconsistent Logic PT”. Zeitschrift für mathematische Logik und Grundlagen der Mathematik. 37, 139-148.
Da Costa, N. C. A., & De Ronde, C. (2013).” The Paraconsistent Logic of Quantum Superpositions”. Foundations of Physics. 43, Issue 7, 845-858.
Da Silva Filho. (2016) “Undulatory Theory with Paraconsistent Logic (Part I): Quantum Logical Model with Two Wave Functions”. Journal of Quantum Information Scienc. 6, 3p. 10.4236/jqis.2016.63012.
Da Silva Filho, J. I., Abe, J. M. (2001). “Emmy: a paraconsistent autonomous mobile robot, in Logic, Artificial Intelligence, and Robotics”. Proc. 2nd Congress of Logic Applied to Technology. LAPTEC’ 2001. J. M. Abe; J. I. Da Silva Filho (editores.), Frontiers in Artificial Intelligence and Its Applications, IOS Press, Amsterdam, Ohmsha, Tókio. 71, 287p
Da Silva Filho, J. I., Abe, J. M. & Torres, G. L. (2008). "Artificial Intelligence with Paraconsistent Analysis Networks - Theory and Applications". Rio de Janeiro, Brasil, Editora LTC.
Da Silva Filho, J. I., Torres, L., Ferrara, L. F. P., Mario, M. C., Santos, M. R., Onuki, A.S., Camargo, J. M. & Rocco, A. (2011). “Paraconsistent Algorithm Extractor of Contradiction Effects”. Jornal of Software Engineering and Applications. 1, 579-584.
Da Silva Filho, J. I., Nunes, C. V., Garcia, D. V., Mario, M.C., Giordano, F., Abe, J. M., Pacheco, M. T. T. & Silveira Jr., L. (2016). “Paraconsistent analysis network applied in the treatment of Raman spectroscopy data to support medical diagnosis of skin cancer”. Medical & Biological Engineering & Computing. 54, 1-15.
Faceli, K., Lorena, A. C., Gama, J. & De Carvalho, A. C. P. L. F. (2019). “Inteligência Artificial – Uma Abordagem de Aprendizado de Máquina”. Rio de Janeiro, Brasil, Editora LTC.
Garcia, D. V., Da Silva Filho, J. I., Silveira Jr., L., Pacheco, M. T. T., Abe, J. M., Carvalho Jr., A., Blos, M. F., Pasqualucci, C. A. G. & Mario, M.C. (2019). “Analysis of Raman spectroscopy data with algorithms based on Paraconsistent Logic for characterization of skin cancer lesions”. Vibrational Spectroscopy. 103, art. number 102929.
Larson, R., Farber, B. (2010). “Estatística Aplicada”. (4a ed.), Editora Pearson Prentice Hall, São Paulo.
Mario, M. C. (2003). “Proposal for the Application of Paraconsistent Artificial Neural Networks as a Signal Classifier using Functional Approach” - Master's Dissertation-UFU, Uberlândia-MG.
Mario, M.C., Abe, J. M., Ortega, N. R. S. & Del Santo Jr., M. (2010). “Paraconsistent Neural Networks as cephalometric diagnosis”: Artificial Organs. 34, serial 7, E215.
Martinez, A. A. G., Minoro, J. A., De Lima, L. A., De Souza, J. S., Bernardini, F. A., De Souza, N. A. & Sakamoto, L. S. (2021). “PANN Component for use in Pattern Recognition in medical diagnostics decision-makin”. 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. 192, 1750–1759.
Norihiro, K. & Heinrich, W. (2012). “Proof theory of Nelson’s paraconsistent logic: A uniform perspective”. Theoretical Computer Science. 415, 1-38. https://doi.org/10.1016/j.tcs.2011.11.001. https://www.sciencedirect.com/science/article/pii/S0304397511008978.
Priest, G. (2002). “Paraconsistent Logic”. In: Gabbay D.M., Guenthner F. (eds) Handbook of Philosophical Logic. Handbook of Philosophical Logic, 6. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0460-1_4.
Silveira Jr., L., Silveira, F. L., Bodanese, B., Zângaro, R. A. & Pacheco, M. T. T. (2012). “Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemical.” Journal of Biomedical Optics. 17, 7p.
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Copyright (c) 2021 Mauricio Conceição Mario; Dorotéa Vilanova Garcia; João Inácio da Silva Filho; Landulfo Silveira Júnior; Heraldo Silveira Barbuy
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