Combining SVMs with Various Feature Selection Strategies

By Yi-Wei Chen, Chih-Jen Lin. 2006

1,117 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

EEG-Based Emotion Recognition in Music Listening (2010, background): “The F -score of the ith feature is defined as follows [ 30 ]:”
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, methodology): “In order to reduce the dimensionality of our feature vector and to use only the most discriminative features, we experimented with feature selection using the F-score (Fisher score) [54]:”
Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm (2017, background): “The higher the F-score, the more discriminative the feature will be [39].”
Heartbeat classification using disease-specific feature selection (2014, methodology): “To tackle the first problem, we accelerate the search process by employing the F-score criterion [25,26] to rank the features and using the backward elimination search strategy.”
A Survey of Modern Questions and Challenges in Feature Extraction (2015, methodology): “Filter methods select a subset of features based on the notion of an importance score that is computed independent of the classifier (Gu et al., 2012; Boutsidis et al., 2009; He et al., 2005; Chen and Lin, 2006).”
A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG (2015, methodology): “In order to determine the discriminative power of each respiratory signal and of each feature, two different statistical tests are used, namely, the F-score [26] and the Kruskal–Wallis test.”
SVM-RFE: selection and visualization of the most relevant features through non-linear kernels (2018, neutral): “Filter methods include the F-score [2, 3].”
Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG (2011, methodology): “5 [31].”
Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases (2011, methodology): “The original F -score is a simple filter technique which measures the discrimination of two sets of real numbers (Chen & Lin, 2005; Akay, 2009; Lee, 2009 .”