Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction

• Autor(a) principal: Mario, Mauricio Conceição
• Data de Publicação: 2021
• Outros Autores: Garcia, Dorotéa Vilanova, Silva Filho, João Inácio da, Silveira Júnior, Landulfo, Barbuy, Heraldo Silveira
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/20830
• topic: Lógica Paraconsistente Anotada; Aprendizaje automático; Clasificación de patrones; Modelo matemático; Efecto de contradicción.; Lógica Paraconsistente Anotada; Aprendizado de máquina; Classificação de padrões; Modelo matemático; Efeito da contradição.; Annotated Paraconsistent Logic; Machine learning; Pattern classification; Mathematical model; Contradiction effect.

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.