A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data

By Qinbao Song, Jingjie Ni, Guangtao Wang. IEEE Transactions on Knowledge and Data Engineering, 2013

632 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a61be375a4d01d7e8791c6fca8db5604d788ff17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Verónica Bolón-Canedo
2. Amparo Alonso-Betanzos
3. Noelia Sánchez-Maroño
4. José Manuel Benítez
5. Sheng Yang
6. Jian Weng
7. Jiawei Luo
8. Xuelian Deng

In Molecular Biology

1. Verónica Bolón-Canedo
2. Amparo Alonso-Betanzos
3. Noelia Sánchez-Maroño
4. Ryan J. Urbanowicz
5. Randal S. Olson
6. Jason H. Moore
7. José Manuel Benítez
8. Sheng Yang

Citations in the corpus

• Recent advances and emerging challenges of feature selection in the context of big data (2015, methodology): “Below we assess recent developments in solutions for high-dimensionality problems in areas such as clustering [33,34], regression [35–37] and classification [38,39].”
• Feature selection in machine learning: A new perspective (2018, methodology): “The FAST [26] method proposed by Song clustering method.”
• Feature selection for text classification: A review (2018, methodology): “Depending on how to generate feature subsets, feature selection methods can be divided into four categories, namely, filter model [10, 83, 110], wrapper model [24, 71], embedded model, and hybrid model [18, 104].”
• Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, neutral): “, 2013) [123] Fast clustering-based feature selection (FAST) 14 Microarray dataset, e.”
• MIFS-ND: A mutual information-based feature selection method (2014, background): “The following properties are trivial based on Peng et al. (2005) and Song, Ni, and Wang (2013).”
• Relief-Based Feature Selection: Introduction and Review (2017, background): “from the UCI repository) (Bins and Draper, 2001; Flórez-López, 2002; Qamar and Gaussier, 2012; Song et al., 2013; Gore and Govindaraju, 2016), (3) some real data analysis that is new or yet to be established as a benchmark (Dess̀ı et al.”
• A review of microarray datasets and applied feature selection methods (2014, methodology): “Leukemia_test 34 7129 71–29% [46] 1999 [119,23,48,108] [3,2,8] Leukemia_train 38 7129 59–41% [46] 1999 [119,23,48,108] [3,2,8] Lung 52 918 75–25% [43] 2001 [101] unknown”