A New Fuzzy Stacked Generalization Technique for Deep learning and Analysis of its Performance

Details A New Fuzzy Stacked Generalization Technique for Deep learning and Analysis of its Performance A New Fuzzy Stacked Generalization Technique for Deep learning and Analysis of its Performance

2012-04-01

arxiv.org/abs/1204.0171v1

Computer Science

Computer Vision and Pattern Recognition

Submitted to IEEE Trans. on PAMI

Scientific paper

We propose a robust Fuzzy Stacked Generalization (FSG) technique for deep learning, which assures a better performance than that of the individual classifiers. FSG aggregates a set of fuzzy k- Nearest Neighbor (k-nn) classifiers in a two-level hierarchy. We make a thorough analysis to investigate the learning mechanism of the suggested deep learning architecture and analyze its performance. We suggest two hypotheses to boost the performance of the suggested architec-ture and show that the success of the FSG highly depends on how the individual classifiers share to learn the samples in the training set. Rather than the power of the individual base layer-classifiers, diversity and cooperation of the classifiers become an important issue to improve the overall performance of the proposed FSG. A weak classifier may boost the overall performance more than a strong classifier, if it is capable of recognizing the samples which are not recognized by the rest of the classifiers. Therefore, the problem of designing a deep learning architecture reduces to the design of the feature spaces for the individual classifiers. The experiments explore the type of the collaboration among the individual classifiers, needed for an improved performance of the suggested deep learning architecture.

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