A Survey on semi-supervised feature selection methods

By R. Sheikhpour, M. Sarram, Sajjad Gharaghani, M. Z. Chahooki. Pattern Recognition, 2017

477 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A comprehensive survey on recent metaheuristics for feature selection (2022, unknown): “investigated semi-supervised methods for feature selection [76].”
CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection (2020, background): “As mentioned in [14], data with a large number of features can affect the learning model that tends to overﬁt and will decrease the performance, increasing memory use, and computational cost for analytic.”
Artificial Intelligence for the Metaverse: A Survey (2022, methodology): “vised learning algorithm and then using the existing labeled data to label the remaining unlabeled data [7].”
A survey on semi-supervised learning (2019, methodology): “We focus on a small number of particularly prominent techniques and refer the reader to the existing literature on feature extraction methods for further information (see, e.g. Guyon and Elisseeff 2006; Sheikhpour et al. 2017).”
Feature selection in machine learning: A new perspective (2018, methodology): “feature selection [66-71], they are mainly focus on specific research fields in feature selection.”