Merging Decision Trees: A Case Study in Predicting Student Performance

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

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

Conference proceedings International

Title: ADVANCED DATA MINING AND APPLICATIONS, ADMA 2014

Pages: 535-548

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

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

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 17 Citations

Scientific classification

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

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14