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IDA
2006
Springer

Classification of symbolic objects: A lazy learning approach

13 years 5 months ago
Classification of symbolic objects: A lazy learning approach
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.
Annalisa Appice, Claudia d'Amato, Floriana Esposit
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where IDA
Authors Annalisa Appice, Claudia d'Amato, Floriana Esposito, Donato Malerba
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