Enhancing data stream predictions with reliability estimators and explanation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

Zoran Bosnic (Autor) Outra; Jaka Demsar (Autor) Outra; Grega Kespret (Autor) Outra; Pedro Pereira Rodrigues (Autor) FMUP; Joao Gama (Autor) FEP; Igor Kononenko (Autor) Outra

Revista

Título: Engineering Applications of Artificial Intelligence

Vol. 34

Páginas: 178-192

ISSN: 0952-1976

Editora: Elsevier

Indexação

ISI Web of Knowledge - 13 Citações

Scopus - 12 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-009-PMJ

DOI: 10.1016/j.engappai.2014.06.001

Abstract (EN): Incremental learning from data streams is increasingly attracting research focus due to many real streaming problems (such as learning from transactions, sensors or other sequential observations) that require processing and forecasting in the real time. In this paper we deal with two issues related to incremental learning - prediction accuracy and prediction explanation - and demonstrate their applicability on several streaming problems for predicting electricity load in the future. For improving prediction accuracy we propose and evaluate the use of two reliability estimators that allow us to estimate prediction error and correct predictions. For improving interpretability of the incremental model and its predictions we propose an adaptation of the existing prediction explanation methodology, which was originally developed for batch learning from stationary data. The explanation methodology is combined with a state-of-the-art concept drift detector and a visualization technique to enhance the explanation in dynamic streaming settings. The results show that the proposed approaches can improve prediction accuracy and allow transparent insight into the modeled concept.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15