Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects

Databases are a key technology for molecular biology which is a very data intensive discipline. Since molecular biological databases are rather heterogeneous, unification and data integration is mandatory to make use of the huge amount of available information. Currently, the most promising approach for integration is the use of ontologies. Since mapping biological entities into ontologies is usually achieved manually or semiautomatically, a system for automatic classification of biological entities into ontologies saves time and effort. Therefore, we present a support vector machine based approach that automatically classifies biological entities into a given ontology. To solve this difficult task, our method copes with the following aspects. Biological entities might belong to more than one class or laced in classes on varying abstraction levels. An object may be described by several representations. Thus, the classifier has to be enabled to draw information from all of them, but mu...

• Type: Conference
• Year: 2004
• Where: SDM
• Authors: Hans-Peter Kriegel, Peer Kröger, Alexey Pryakhin, Matthias Schubert
• Keywords: Biological Entities, Data Mining, Molecular Biological Databases, SDM 2004, Support Vector Machines