A Survey on Feature Selection

By Jianyu Miao, Lingfeng Niu. 2016

301 citations recorded by the source corpus.

Abstract Feature selection, as a dimensionality reduction technique, aims to choosing a small subset of the relevant features from the original features by removing irrelevant, redundant or noisy features. Feature selection usually can lead to better learning performance, i.e., higher learning accuracy, lower computational cost, and better model interpretability. Recently, researchers from computer vision, text mining and so on have proposed a variety of feature selection algorithms and in terms of theory and experiment, show the effectiveness of their works. This paper is aimed at reviewing the state of the art on these techniques. Furthermore, a thorough experiment is conducted to check if the use of feature selection can improve the performance of learning, considering some of the approaches mentioned in the literature. The experimental results show that unsupervised feature selection algorithms benefits machine learning tasks improving the performance of clustering.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

Unsupervised Adversarial Invariance (2018, methodology): “feature selection mechanism for neural networks, which can work on both raw data (such as images) and feature-sets (e.g., frequency features computed from raw text). Popular feature selection methods [16] incorporate information-theoretic measures or use supervised methods to score features with the”