Methods for feature selection in a learning machine

Details Methods for feature selection in a learning machine Methods for feature selection in a learning machine

2007-10-30

2009-11-24

Holmes, Michael B (Department: 2129)

Data processing: artificial intelligence

Fuzzy logic hardware

Reexamination Certificate

active

07624074

ABSTRACT:
In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (l0-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score and transductive feature selection. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection.

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