Search-based class discretization

• Type: Article in International Scientific Journal
• Date: 1997

Authors

Torgo, L (Author) FEP; Gama, J (Author) FEP

Journal

Title: Lecture Notes in Computer Science

Vol. 387

Pages: 266-273

ISSN: 0302-9743

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 17 Citations

Scopus - 23 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-001-CR7

DOI: 10.1007/3-540-62858-4_91

Abstract (EN): We present a methodology that enables the use of classification algorithms on regression tasks. We implement this method in system RECLA that transforms a regression problem into a classification one and then uses an existent classification system to solve this new problem. The transformation consists of mapping a continuous variable into an ordinal variable by grouping its values into an appropriate set of intervals. We use misclassification costs as a means to reflect the implicit ordering among the ordinal values of the new variable. We describe a set of alternative discretization methods and, based on our experimental results, justify the need for a search-based approach to choose the best method. Our experimental results confirm the validity of our search-based approach to class discretization, and reveal the accuracy benefits of adding misclassification costs.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8