Predicting outliers

• Type: Article in International Scientific Journal
• Date: 2003

Authors

Torgo, L (Author) FEP; Ribeiro, R (Author) Other

Journal

Title: Lecture Notes in Computer Science

Vol. 387

Pages: 447-458

ISSN: 0302-9743

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 16 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-000-K37

DOI: 10.1007/978-3-540-39804-2_40

Abstract (EN): This paper describes a method designed for data mining applications where the main goal is to predict extreme and rare values of a continuous target variable, as well as to understand under which conditions these values occur. Our objective is to induce models that are accurate at predicting these outliers but are also interpretable from the user perspective. We describe a new splitting criterion for regression trees that enables the induction of trees achieving these goals. We evaluate our proposal on several real world problems and contrast the obtained models with standard regression trees. The results of this evaluation show the clear advantage of our proposal in terms of the evaluation statistics that are relevant for these applications.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12