Paraconsistent Feature Engineering [Lecture Notes

Abstract

Today's modern world is filled with uncertainties and contradictions. As artificial intelligence (AI) advances, machines are frequently expected to mimic the human brain and, consequently, face the conflicts associated with this task. To overcome them, feature engineering has emerged as the field of science responsible for turning raw data into relevant input information, setting up classifiers in the fused digital signal processing (DSP) and pattern recognition (PR) domain. Despite the ongoing efforts to improve feature learning, handcrafted extraction still plays a very important role. In this context, a careful choice of features is extremely relevant for creating an accurate classification. This article sheds light on the problem of feature quality by using a nonclassical logical system capable of handling conflictive situations. It is known as paraconsistent logic (PL).