Towards Automatic Generation of Metafeatures

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Pinto, F (Author) Other; Carlos Soares (Author) FEUP; João Mendes-Moreira (Author) FEUP

Conference proceedings International

Title: ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2016, PT I

Pages: 215-226

20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

Univ Auckland, Auckland, NEW ZEALAND, APR 19-22, 2016

Indexing

ISI Web of Knowledge - 35 Citations

Scopus - 39 Citations

Scientific classification

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

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00K-BFQ

DOI: 10.1007/978-3-319-31753-3_18

Abstract (EN): The selection of metafeatures for metalearning (MtL) is often an ad hoc process. The lack of a proper motivation for the choice of a metafeature rather than others is questionable and may originate a loss of valuable information for a given problem (e.g., use of class entropy and not attribute entropy). We present a framework to systematically generate metafeatures in the context of MtL. This framework decomposes a metafeature into three components: meta-function, object and post-processing. The automatic generation of metafeatures is triggered by the selection of a meta-function used to systematically generate metafeatures from all possible combinations of object and post-processing alternatives. We executed experiments by addressing the problem of algorithm selection in classification datasets. Results show that the sets of systematic metafeatures generated from our framework are more informative than the non-systematic ones and the set regarded as state-of-the-art.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12