Feature Selection Meets Deep Learning

Dear Colleagues,

Over the last few years, feature ranking and selection (FRS) has attracted a lot of attention in solving computer vision and pattern recognition problems, from vision to language. FRS techniques have been playing a central role in identifying the most relevant cues from huge amounts of otherwise meaningless data. However, with the advent of representation learning and deep learning there has been a major shift in the way features, or representations, are designed (i.e., the learning of data-driven representations). As a result, conventional FRS strategies may not be the most suitable for deep neural networks (DNNs) and novel strategies might be explored for a more natural integration.

The primary focus of this Special Issue will be on feature selection and deep learning, that is the question of how deep learning models can be imbued with FRS strategy. In fact, FRS can help to regulate the elaborate learning process behind DNNs by (i) simultaneously learning which features are informative in the process, (ii) reducing the significant redundancy in deep convolutional neural networks (CNNs) by pruning neurons, (iii) regulating dynamically the dropout factor to improve the prediction performance, and so on.

This Special Issue calls for contributions that target the study and analysis of FRS strategies for deep learning models from both theoretical and application perspectives. The topics of interest include, but are not limited, to the following:

• Pruning networks using feature selection strategies
• Feature selection based dropout
• Feature selection layers in CNNs
• Relevancy and residual DNNs
• Deep feature selection
• Feature selection using DNNs
• Please refer to the submission page for the submission guidelines.

Dr. Giorgio Roffo
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Feature selection
• Deep learning
• representation learning
• learning (artificial intelligence)
• neural nets
• feature extraction
• space dimensionality reduction
• sparsity
• network pruning
• Dropout
• Filtering