Multi-layer Perceptrons with Embedded Feature Selection with Application in Cancer Classification ∗

∗ This research was supported by the National Natural Science Foundation of China under Grant No. 60372050. Abstract — This paper proposed a novel neural network model, named multi-layer perceptrons with embedded feature selection (MLPs-EFS), where feature selection is incorporated into the training procedure. Compared with the classical MLPs, MLPs-EFS add a preprocessing step where each feature of the samples is multiplied by the corresponding scaling factor. By applying a truncated Laplace prior to the scaling factors, feature selection is integrated as a part of MLPs-EFS. Moreover, a variant of MLPs-EFS, named EFS+MLPs is also given, which perform feature selection more flexibly. Application in cancer classification validates the effectiveness of the proposed algorithms.

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