Classification of symbolic objects: A lazy learning approach

Issue title: Analysis of Symbolic and Spatial Data

Guest editors: Paula Britox and Monique Noirhomme-Fraiturey

Article type: Research Article

Authors: Appice, Annalisa | D'Amato, Claudia | Esposito, Floriana | Malerba, Donato

Affiliations: Dipartimento di Informatica, Università degli Studi, via Orabona, 4, 70125 Bari, Italy. E-mail: appice@di.uniba.it, claudia.damato@di.uniba.it, esposito@di.uniba.it, malerba@di.uniba.it | [x] Faculdade de Economia, University of Porto, Rua Dr. Roberto Frias, 4200-464 Porto, Portugal. E-mail: mpbrito@fep.up.pt | [y] Institut d'Informatique, Facultés Universitaires Notre Dame de la Paix, Rue Grandgagnage, 21, B-5000 Namur, Belgium. E-mail: monique.noirhomme@info.fundp.ac.be

Abstract: Symbolic data analysis aims at generalizing some standard statistical data mining methods, such as those developed for classification tasks, to the case of symbolic objects (SOs). These objects synthesize information concerning a group of individuals of a population, eventually stored in a relational database, and ensure confidentiality of original data. Classifying SOs is an important task in symbolic data analysis. In this paper a lazy-learning approach that extends a traditional distance weighted k-Nearest Neighbor classification algorithm to SOs, is presented. The proposed method has been implemented in the system SO-NN (Symbolic Objects Nearest Neighbor) and evaluated on symbolic datasets.

DOI: 10.3233/IDA-2006-10402

Journal: Intelligent Data Analysis, vol. 10, no. 4, pp. 301-324, 2006