Feature Extraction, Foundations and Applications

By Y. Chen, Chih-Jen Lin. 2006

1,479 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a71b3b212153139fdb15f16edfc2516b00d13ceb

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Citations in the corpus

A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification (2014, methodology): “, KFS) outperforms BAHSIC [37] , FS [19], FSFS [58], [59], and RFE [45], [60].”
Variable selection in functional data classification: a maxima-hunting proposal (2013, methodology): “s used in a more general way, to include the generation of new features. Also, the methods we have called \intrinsic" are often denoted as \lter methods" in machine learning. The monograph Guyon et al. (2006) contains a complete survey on feature extraction (including selection) from the p”
Materialization optimizations for feature selection workloads (2014, methodology): “Embedded methods wire feature selection into a statistical model [21].”
Neighborhood Component Feature Selection for High-Dimensional Data (2012, methodology): “In particular, many SVM-based embedded methods have been proposed [3], [4].”
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016, methodology): “, [21–25] — see [26] for a survey) none have studied the use of DBNs as a method for deep feature construction in the context of anomaly detection, i.”
A review of feature selection methods on synthetic data (2013, methodology): “Besides this classification, feature selection methods can also be divided into three models: filters, wrappers and embedded methods [6].”
Steganalysis by Subtractive Pixel Adjacency Matrix (2009, unknown): “In theory, finding the optimal subset of features is an NP-complete problem [9], which frequently suffers from overfitting.”
Feature selection via sensitivity analysis of SVM probabilistic outputs (2008, methodology): “In contrast, embedded-based methods exploit the knowledge of the specific structure of the learning algorithm (Guyon and Elisseeff 2003; Lal et al. 2006) and cannot be separated from it.”
Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines (2014, methodology): “Common wrapper strategies are Sequential Forward Selection (SFS) and Sequential Backward Elimination (SBE) [14] which evaluate the selected feature sets based on a particular classiﬁcation method.”
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? (2017, methodology): “The conventional wisdom is that even a feature that is redundant might be highly relevant and hence, unavoidable in the mix [16].”
A user's guide to support vector machines. (2010, methodology): “After having defined a set of features it is instructive to perform feature selection: remove features that do not contribute to the accuracy of the classifier [26, 27].”