Application-Independent Feature Construction from Noisy Samples

When training classifiers, presence of noise can severely harm their performance. In this paper, we focus on "non-class" attribute noise and we consider how a frequent fault-tolerant (FFT) pattern mining task can be used to support noise-tolerant classification. Our method is based on an application independent strategy for feature construction based on the so-called *** -free patterns. Our experiments on noisy training data shows accuracy improvement when using the computed features instead of the original ones.

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