Special Issue "Feature Selection for High-Dimensional Data"
Deadline for manuscript submissions: 31 October 2017
Feature selection has been embraced as one of the high activity research areas during the last few years, because of the appearance of datasets containing hundreds of thousands of features. Therefore, feature selection was deemed as a great tool to better model the underlying process of data generation, as well as to reduce the cost of acquiring the features. Furthermore, from the Machine Learning perspective, given that feature selection can reduce the dimensionality of the problem, it can be used for maintaining or even improving the algorithms’ performance, while reducing computational costs. Nowadays, the advent of Big Data has brought unprecedented challenges to machine learning researchers, who now have to deal with huge volumes of data, in terms of both instances and features, making the learning task more complex and computationally demanding than ever. Specifically, when dealing with an extremely large number of features, learning algorithms’ performance can degenerate due to overfitting; learned models decrease their interpretability as they become more complex; and speed and efficiency of the algorithms decline in accordance with size. A vast body of feature selection methods exists in the literature, including filters based on distinct metrics (e.g., entropy, probability distributions or information theory) and embedded and wrapper methods using different induction algorithms. However, some of the most used algorithms were developed when dataset sizes were much smaller, and nowadays they cannot scale well, producing a need to readapt these successful algorithms to be able to deal with Big Data problems.
In this Special Issue, we invite investigators to contribute with their recent developments in feature selection methods for high-dimensional settings, as well as review articles that will stimulate the continuing efforts to understand the problems usually encountered in this field.
Topics of interest include, but are not limited to:
- New feature selection methods
- Ensemble methods for feature selection
- Feature selection to deal with microarray data
- Parallelization of feature selection methods
- Missing data in the context of feature selection
- Feature selection applications
Dr. Verónica Bolón Canedo
Dr. Noelia Sánchez-Maroño
Dr. Amparo Alonso-Betanzos
Manuscript Submission Information
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- Feature selection
- Ensemble feature selection
- Embedded methods