Mining logic models in the presence of noisy data.
Giovanni Felici, Emanuel Weitschek · ISAIM · 2012
In this work we consider a method for the extraction of knowledge expressed in Disjunctive Normal Form (DNF) from data. The method is mainly designed for classification purposes, and is based on three main steps: Discretization, Feature Selection, and Formula Extraction. The three steps are formulated as optimization problems and solved with ad hoc algorithmic strategies. When used for classification purposes, the proposed approach is designed to perform exact separation of training data and can thus be exposed to overfitting when a significant amount of noise is present. We analyze the main problems that may arise when this method deals with noisy data and propose extensions for the three steps of the method.