Feature selection for support vector machines by means of genetic algorithm

By Holger Fröhlich, Olivier Chapelle, Bernhard Schölkopf. Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence, 2003

350 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0d28c0390b21244cc52e9af856249cb601f6b22d

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Citations in the corpus

MIFS-ND: A mutual information-based feature selection method (2014, background): “Anomaly detection in real-time is a challenging problem for network security researchers and practitioners.”
First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation (2019, methodology): “This non-greedy method algorithm aximizes model prediction performance and minimizes model omplexity simultaneously [74 , 75] .”
Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images (2006, background): “…the idea of combining SVMs with GAs for performance improvement in remote sensing problems is novel, it has been explored very recently in other application contexts including biomedical data processing [21]–[23], biochemical analysis [24], face authentication [25], and intrusion detection [26].”
Evolutionary tuning of multiple SVM parameters (2005, methodology): “This includes the recent applications of genetic algorithms for feature selection of SVMs [6, 7 ,14,17].”
Towards Optimal Descriptor Subset Selection with Support Vector Machines in Classification and Regression (2004, methodology): “Often used heuristics are backward elimination and forward selection [2] [10] [16], but also stochastic search methods like Genetic Algorithms [8] [22] [24] [27] [28].”
A GA-based feature selection and parameters optimizationfor support vector machines (2006, background): “These two problems are crucial, because the feature subset choice influences the appropriate kernel parameters and vice versa (Fröhlich and Chapelle, 2003).”
A hybrid approach of differential evolution and artificial bee colony for feature selection (2016, methodology): “…search methods including tabu search (TS), simulated ), genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) evolution (DE), and artificial bee colony (ABC) are used to search feature subset selecting (sub)optimal feature set (Frohlich et al., 2003).”
Support vector machines combined with feature selection for breast cancer diagnosis (2009, background): “These problems are crucial because the feature subset choice influences the appropriate kernel parameters and vice versa (Frohlich et al., 2003).”