New Approaches to Fuzzy-Rough Feature Selection

By Richard Jensen, Qiang Shen. IEEE transactions on fuzzy systems, 2009

566 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f7ae5d7c3bb6ba22557af13d30dd24570618d395

How researchers see this paper

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

In Computational Theory and Mathematics

1. Qing Xu
2. Jianhua Dai
3. Pradipta Maji
4. Partha Garai
5. Ren-hong Diao
6. Kai Keng Ang
7. Cuntai Guan
8. Haihong Zhang

In Information Systems

1. Qing Xu
2. Jianhua Dai
3. Pradipta Maji
4. Partha Garai
5. Ren-hong Diao
6. Cuntai Guan
7. Haihong Zhang
8. Kai Keng Ang

In Management Science and Operations Research

1. Qing Xu
2. Jianhua Dai
3. Qiang Shen
4. Pradipta Maji
5. Partha Garai
6. Ren-hong Diao
7. Eric Wai Ting Ngai
8. James Nga-Kwok Liu

Citations in the corpus

• Fuzzy–Rough Simultaneous Attribute Selection and Feature Extraction Algorithm (2013, methodology): “The generalized theories of rough-fuzzy computing have been applied successfully to feature selection of real valued data set [7], [11], [14], [15], [16], [17], [18].”
• Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008, methodology): “The new Fuzzy-Rough set-based Feature Selection (FRFS2) algorithm [23],[24] that is based on the filter approach is used in this paper.”
• Anomaly detection in wireless sensor networks: A survey (2011, background): “, 2005), and fuzzy and rough sets (Jensen and Shen, 2009).”
• Application of decision-making techniques in supplier selection: A systematic review of literature (2013, methodology): “Such methods had been successfully applied for attribute reduction (Tsang, Chen, Yeung, Wang, & Lee, 2008), feature selection (Jensen & Shen, 2009), classification and prediction (Chai & Liu, 2012).”
• Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification (2013, methodology): “Therefore, it has been playing an important role in attribute selection (also called attribute reduction or feature selection ) [18] [24] [29] [31] [40] [45] [48] [49] [57] [60].”
• Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection (2014, unknown): “Jensen and Shen et al have also proposed a lot of promising feature selection approaches based on rough set and fuzzy rough set theory [20], [21].”
• Feature Selection With Harmony Search (2012, methodology): “The following defines the fuzzy lower and upper approximations: μRP X(x) = inf y∈U I (μRP (x, y), μX(y)) (3) μRP X(x) = sup y∈U T (μRP (x, y), μX(y)) (4) where I is a fuzzy implicator, T is a t-norm, and RP is the fuzzy similarity relation induced by the subset of features P μRP (x, y) = Ta∈P {μRa(x”