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

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

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

Conference proceedings International

Title: Proceedings of the ECMLPKDD 2015 Doctoral Consortium

Pages: 216-225

ECMLPKDD 2015 Doctoral Consortium

Porto, Portugal, 7 de Setembro de 2015

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

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.

Language: English

Type (Professor's evaluation): Scientific