LAUSR

Pattern recognition algorithms have introduced increasingly sophisticated solutions. However, many datasets are far from perfect; for example, they may include inconsistencies and have missing data, which may interfere with the classification process. Thus, the use of paraconsistent logic can provide a compelling quantitative analysis approach in classification algorithms because it deals directly with inaccurate, inconsistent and incomplete data. Paraconsistent logic is considered a nonclassical logic, which enables the processing of contradictory signals in its theoretical structure without invalidating the conclusions. In this context, the proposed approach aggregates the power of hybrid classifiers, the low noise susceptibility of the random forest approach and the robustness of paraconsistent logic to provide an intelligent treatment of contradictions and uncertainties in datasets. The proposed method is called paraconsistent random forest. The computational results demonstrated that paraconsistent random forest could classify several databases with satisfactory accuracy in comparison with state-of-the-art methods, namely, LDA, KNN, and SVM. Regarding imperfect datasets, the proposed approach significantly outperforms most of these methods in terms of prediction accuracy.