Exploring the impact of ordering models in merging decision trees: a case study in education

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Pedro Strecht (Autor) FEUP; João Mendes-Moreira (Autor) FEUP; Carlos Soares (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings of the ECMLPKDD 2015 Doctoral Consortium

Páginas: 216-225

ECMLPKDD 2015 Doctoral Consortium

Porto, Portugal, 7 de Setembro de 2015

Classificação Científica

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

Abstract (EN): Merging decision trees models has been, so far, motivated as a way to avoid both transporting data sets on distributed locations or training very large data sets. This paper presents a novel rationale which is the need to generalize knowledge by grouping models consumed across different decision levels in a non-distributed environment and propose a methodology for this goal. The approach is evaluated using data from the University of Porto, in the context of predicting the success/failure of students in courses. The experiments focus mainly on the impact of the order of models on the overall performance of merged models. Directions of unexplored issues for future research are also discussed.

Idioma: Inglês

Tipo (Avaliação Docente): Científica