A Similarity Function with Local Feature Weighting for Structured Data
نویسندگان
چکیده
The application of learning approaches as Kernel or Instance Based methods to tree structured data requires the definition of similarity functions able to deal with such data. A new similarity function for nearest prototype classification in relational data that follows a tree structure is defined in this paper. Its main characteristic is its capability to weight the importance of the different data features in different areas of the feature space. This work is built over two previous ideas: a similarity function for Local Feature Weighting (LFW), and a Relational Nearest Prototype Classification algorithm (RNPC).
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