An iterative process for building learning curves and predicting relative performance of classifiers

• Type: Chapter or Part of a Book
• Date: 2007

Authors

Rui Leite (Author) FEP; Pavel Brazdil (Author) FEP

Book

Title: Progress in Artificial Intelligence

Pages: 87-98

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 15 Citations

Other information

Authenticus ID: P-004-EF0

DOI: 10.1007/978-3-540-77002-2_8

Abstract (EN): This paper concerns the problem of predicting the relative performance of classification algorithms. Our approach requires that experiments are conducted on small samples. The information gathered is used to identify the nearest learning curve for which the sampling procedure was fully carried out. This allows the generation of a prediction regarding the relative performance of the algorithms. The method automatically establishes how many samples are needed and their sizes. This is done iteratively by taking into account the results of all previous experiments - both on other datasets and on the new dataset obtained so far. Experimental evaluation has shown that the method achieves better performance than previous approaches.

Language: English

Type (Professor's evaluation): Scientific

Notes: Part of the Lecture Notes in Computer Science book series (LNCS, volume 4874) Print ISBN: 978-3-540-77000-8

No. of pages: 12