Merging Decision Trees: A Case Study in Predicting Student Performance

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

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

Ata de Conferência Internacional

Título: ADVANCED DATA MINING AND APPLICATIONS, ADMA 2014

Páginas: 535-548

10th International Conference on Advanced Data Mining and Applications (ADMA)

Guilin, PEOPLES R CHINA, DEC 19-21, 2014

Indexação

ISI Web of Knowledge - 10 Citações

Scopus - 17 Citações

Classificação Científica

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

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

Outras Informações

ID Authenticus: P-00A-1BD

DOI: 10.1007/978-3-319-14717-8_42

Abstract (EN): Predicting the failure of students in university courses can provide useful information for course and programme managers as well as to explain the drop out phenomenon. While it is important to have models at course level, their number makes it hard to extract knowledge that can be useful at the university level. Therefore, to support decision making at this level, it is important to generalize the knowledge contained in those models. We propose an approach to group and merge interpretable models in order to replace them with more general ones without compromising the quality of predictive performance. We evaluate our approach using data from the U. Porto. The results obtained are promising, although they suggest alternative approaches to the problem.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14