Dissertação de Jorge Cristhian Chamby Diaz

Aluno: Jorge Cristhian Chamby Diaz
Orientadora: Profª. Drª. Ana Lucia Cetertich Bazzan
Coorientadora: Profª. Drª. Mariana Recamonde Mendoza Guerreiro

Título: An Incremental Gaussian Mixture Network for Data Stream Classification in Non-Stationary Environments
Linha de Pesquisa: Inteligência Artificial

Data: 24/01/2018
Hora: 14h
Local: Prédio 43412 – AUD-1 (Auditório 1), Instituto de Informática.

Banca Examinadora:
Prof. Dr. Gustavo Enrique de Almeida Prado Alves Batista (USP via videoconferência)
Prof. Dr. Milton Roberto Heinen (UNIPAMPA via videoconferência)
Prof. Dr. Adriano Velasque Werhli (FURG via videoconferência)

Presidente da Banca: Profª. Drª. Ana Lucia Cetertich Bazzan

Abstract: Data stream classification poses many challenges for the data mining community when the environment is non-stationary. The greatest challenge in learning classifiers from data stream relates to adaptation to the concept drifts, which occur as a result of changes in the underlying concepts. Two main ways to develop adaptive approaches are ensemble methods and incremental algorithms. Ensemble method plays an important role due to its modularity, which provides a natural way of adapting to change. Incremental algorithms are faster and have better anti-noise capacity than ensemble algorithms, but have more restrictions on concept drifting data streams. Thus, it is a challenge to combine the flexibility and adaptation of an ensemble classifier in the presence of concept drift, with the simplicity of use found in a single classifier with incremental learning. With this motivation, in this dissertation we propose an incremental, online and probabilistic algorithm for classification as an effort of tackling concept drifting. The algorithm is called IGMN-NSE and is an adaptation of the IGMN algorithm. The two main contributions of IGMN-NSE in relation to the IGMN are: predictive power improvement for classification tasks and adaptation to achieve a good performance in non-stationary environments. Extensive studies on both synthetic and real-world data demonstrate that the proposed algorithm can track the changing environments very closely, regardless of the type of concept drift.

Keywords: Incremental learning, Data streams classification, Concept drift, Gaussian mixture models.