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Active Learning with Local Models
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Generalized relevance learning vector quantization
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Neural Processing Letters
Local metric adaptation for soft nearest prototype classification to classify proteomic data
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Neurocomputing
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In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation procedure. The proposed active learning strategy aims on an improved generalization ability of the final model. This is achieved by usage of an adaptive query strategy which is more adequate for supervised learning than a simple random approach. Beside an improved generalization ability the method also improves the speed of the learning procedure which is especially beneficial for large data sets with multiple similar items. The algorithm is based on the idea of selecting a query on the borderline of the actual classification. This can be done by considering margins in an extension of learning vector quantization based on an appropriate cost function. The proposed active learning approach is analyzed for two kinds of learning vector quantizers the supervised relevance neural gas and the supervised nearest prototype classifier, but is applicable for a broader set of prototype-based learning approaches too. The performance of the query algorithm is demonstrated on synthetic and real life data taken from clinical proteomic studies. From the proteomic studies high-dimensional mass spectrometry measurements were calculated which are believed to contain features discriminating the different classes. Using the proposed active learning strategies, the generalization ability of the models could be kept or improved accompanied by a significantly improved learning speed. Both of these characteristics are important for the generation of predictive clinical models and were used in an initial biomarker discovery study.