Fast Supervised Selection of Prototypes for Metric-Based Learning

摘要

A crucial factor for successful learning is the finding of more convenient representations for a problem, such that subsequent processing can be delivered to linear or non-linear modeling methods. Similarity functions are a flexible way to express knowledge about a problem and to capture meaningful relations of data in input space. In this paper we use similarity functions to find an alternative data representation which is then reduced by selecting a subset of relevant prototypes, in a supervised way. The idea is tested in a set of modelling problems, characterized by a mixture of data types and different amounts of missing values. The results demonstrate competitive or better performance than traditional methods in terms of prediction error and sparsity of the representation.