Detectores de mudança de conceito por meio do mapeamento espacial do fluxo de dados usando quadtree

• Ano de defesa: 2022
• Autor(a) principal: Rodrigo Amador Coelho
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICA; Programa de Pós-Graduação em Engenharia Elétrica; UFMG
• Palavras-chave em Português: Fluxo de dados; Mudança de conceito; Quadtree; Detector de mudança de conceito; Classificação; Engenharia elétrica; Processamento eletrônico de dados; Algoritmos
• Link de acesso: http://hdl.handle.net/1843/52668

Online learning is a complex task, especially when the data stream changes its distribution over time. It has been a challenge to monitor and detect these changes to preserve the learning algorithm performance. This work presents two novels drift detection methods built from a different perspective of other preexisting detectors from literature.It analyzes the space occupied by the data, assuming that it would be immutable unless changes in this space occur among data of different classes. Data are mapped into a quadtree-based memory structure that provides knowledge about which class (label) is dominant in a given region of the feature space. The proposed method QT detects a drift by checking whether data assigned to a given class occupy spaces considered relevant to the other class. The QTS, on the other hand, detects a concept drift when it identifies a significant increase in the increment of data in one of the classes. The proposed methods were evaluated on binary classification benchmark problems. Results show that our methods were competitive with well-known drift detectors from literature.