Título: A Rough Sets-based Rule Induction for Numerical Datasets

Data: 20/12/2018

Horário: 10:00h

Local: Sala de Seminários - Bloco 952

Resumo:

Drawing reasonable conclusions from real-world data has been a challenge owing to diverse factors related to the quality of information. In order to handle these problems, the Rough Sets Theory, which deals with inconsistency through the approximation of data sets, was proposed. Among the applications of rough sets, their use in learning processes is highlighted due to their capacity to produce interpretable classification models. Despite their success, some of the most commonly used rough sets based methods are designed to work with categorical input data. This design choice can severely limit their application to real world problems. In this work, we show some strategies to allow models based on approximate sets to work with numeric data sets.

Banca:

• Prof. Dr. João Fernando Lima Alcântara (MDCC/UFC - Orientador)
• Prof. Dr. João Paulo Pordeus Gomes (MDCC/UFC)
• Prof. Dr. César Lincoln Cavalcante Mattos (UFC)