Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
By Isabelle M Guyon, Steve R. Gunn, M. Nikravesh, Lotfi A. Zadeh. 2006
699 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/9d62d9fa8172f2e65d2cd41ab76a51be977cbf1c
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- Alexander R. Statnikov
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- Subramani Mani
Citations in the corpus
- Feature selection via sensitivity analysis of SVM probabilistic outputs (2008, methodology): “Generally, embedded-based methods are superior in performance relative to filter-based or wrapper-based methods but carry with them a heavier computational load (Guyon et al. 2006b).”
- Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, background): “Excellent recent reviews of feature selection can be found in Guyon et al. (2006a), Guyon and Elisseeff (2003) and Liu and Motoda (1998).”
- Study of Ultra-High Energy Cosmic Ray composition using Telescope Array’s Middle Drum detector and surface array in hybrid mode (2014, unknown): “Training involves training set construction, feature construction, decision tree population, and feature selection [25].”
- Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease (2012, background): “A reduced feature subset also facilitates inference, enabling one to gain insights into the problem via analysis of the most predictive features [25], [26].”
- Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013, background): “The aim of feature extraction is to discover the main characteristics of a data set which are identically representatives of the original data [63].”
- Differentially Private Algorithms for Empirical Machine Learning (2014, background): “Purity Index [11]: The purity index for a featureF , denoted by PI(F,D), is defined as: PI(F,D) = max { |nF=1∧L=1 − nF=1∧L=0|, |nF=0∧L=1 − nF=0∧L=0| } (4) PI(F,D) is the same asDC(F,D), except that it also considers the difference in counts when the feature takes the value0.”