A survey of feature selection and feature extraction techniques in machine learning

By Samina Khalid, Tehmina Khalil, Shamila Nasreen. 2014

1,044 citations recorded by the source corpus.

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

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Citations in the corpus

Relational autoencoder for feature extraction (2017, methodology): “The initial feature extraction methods [9] are proposed based on projection, mapping input features in the original high-dimensional space to a new low-dimensional space by minimizing information loss.”
Big data for cyber physical systems in industry 4.0: a survey (2019, methodology): “Besides research on speci ﬁ c prediction models, feature selection (Chandrashekar and Sahin 2014; Khalid, Khalil, and Nasreen 2014) and ensemble (Kuncheva and Rodríguez 2014) are two general techniques can be applied to any prediction models to improve the prediction performance.”
Mel Frequency Cepstral Coefficient and its Applications: A Review (2022, neutral): “Dimensional reduction can be conducted in using feature extraction and feature selection techniques [2].”
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, methodology): “An overview of feature selection techniques is given in the studies by Khalid et al. 168 and Chandrashekar and Sahin.”
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques (2023, methodology): “Although the 2D CNN is more straightforward than the auto-encoder network, it performed better in feature extraction and learning, and according to what was previously mentioned, it uses all the parameters for learning [37].”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “In contrast, feature selection methods select a subset of the original variables that are determined to be valuable for the classi ﬁ cation (Khalid, Khalil, and Nasreen 2014; Liu and Motoda 1998; Mather 2004; Pal and Foody 2010).”
Multi-Classification of Brain Tumor Images Using Deep Neural Network (2019, background): “Moreover, this job consumes much time and is prone to error while handling large scale of data [14].”