Adaptive Data Embedding Framework for Multiclass Classification


The target of this paper is the look of an engine for the automatic generation of supervised manifold embedding models. It proposes a modular and adaptive knowledge embedding framework for classification, known as DEFC, which realizes in numerous stages together with initial information preprocessing, relation feature generation and embedding computation. For the computation of embeddings, the ideas of friend closeness and enemy dispersion are introduced, to higher control at local level the relative positions of the intraclass and interclass information samples. These are shown to be general cases of the world information setup utilized within the Fisher criterion, and are used for the construction of various optimization templates to drive the DEFC model generation. For model identification, we tend to use a straightforward but effective bilevel evolutionary optimization, which searches for the optimal model and its best model parameters. The effectiveness of DEFC is demonstrated with experiments using noisy synthetic datasets possessing nonlinear distributions and real-world datasets from completely different application fields.

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