Orthogonal forward selection and backward elimination algorithms for feature subset selection
By Kezhi Mao. 2004
231 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Control and Systems Engineering
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In Analytical Chemistry
In Control and Systems Engineering
Citations in the corpus
- Supersparse linear integer models for optimized medical scoring systems (2015, methodology): “Sparse linear classifiers can also be produced using feature selection algorithms (Guyon and Elisseeff, 2003, Mao, 2004), though these algorithms cannot guarantee an optimal balance between accuracy and sparsity as they typically rely on greedy optimization (with some exceptions, see e.g. Bradley…”
- Methods and Models for Interpretable Linear Classification (2014, methodology): “In both cases, sparsity can be further induced using feature selection algorithms [36, 50, 62, 61, 91, 102].”
- Supersparse Linear Integer Models for Interpretable Classification (2013, unknown): “Sparsity can also be induced using feature selection algorithms [29, 37, 47, 46, 67, 77].”
- Computational Methods in Drug Discovery (2014, unknown): “…such as information gain (Kent, 1983) and F-score (Chen and Lin, 2006), sequential feature forward selection or feature backward elimination (Mao, 2004), genetic algorithm (Davis, 1991; Goodarzi et al., 2009), swarm optimization (Goodarzi et al., 2009), and input sensitivity analysis…”
- Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats (2020, methodology): “Thus, such methods are more practical and faster than optimal selection methods (i.e., exhaustive search) (Mao 2004).”