Fuzzy probabilistic approximation spaces and their information measures

By Qinghua Hu, Daren Yu, Zongxia Xie, Jinfu Liu. IEEE transactions on fuzzy systems, 2006

316 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/987fd15da97a44bd898faf2a9dff4adffbd89822

How researchers see this paper

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

In Artificial Intelligence

1. Richard Jensen
2. Yan Zhao
3. Yiyu Yao
4. Y. Qian
5. Qinghua Hu
6. Daren Yu
7. Jiye Liang
8. Witold Pedrycz

In Computational Theory and Mathematics

1. Qinghua Hu
2. Daren Yu
3. Yiyu Yao
4. Y. Qian
5. Richard Jensen
6. Qiang Shen
7. Yan Zhao
8. Zongxia Xie

In Management Science and Operations Research

1. Yiyu Yao
2. Qiang Shen
3. Richard Jensen
4. Qinghua Hu
5. Daren Yu
6. Y. Qian
7. Chuangyin Dang
8. Jiye Liang

Citations in the corpus

• Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “Feature selection algorithms, based on the generalization of fuzzy approximation spaces to fuzzy probability approximation spaces, are introduced in [141].”
• Neighborhood rough set based heterogeneous feature subset selection (2008, methodology): “extended Shannon’s entropy to measure the information quantity in a set of fuzzy sets [9] and applied the proposed measure to calculate the uncertainty in fuzzy approximation spaces and used it to reduce heterogeneous data [10], where numerical attributes induce fuzzy relations and symbolic features”
• Attribute reduction in decision-theoretic rough set models (2008, methodology): “The proposals include probabilistic rough set models [12,20,28,40,44,45], decisiontheoretic rough set models [43,47,48], variable precision rough set models [52], rough membership functions [25], parameterized rough set models [27,30], and Bayesian rough set models [8,9,32,33].”
• Positive approximation: An accelerator for attribute reduction in rough set theory (2010, methodology): “presented a new entropy to measure of the information quantity in fuzzy sets [21] and applied this particular measure to reduce hybrid data [22].”
• Fuzzy–Rough Simultaneous Attribute Selection and Feature Extraction Algorithm (2013, background): “The generalized theories of rough–fuzzy computing have been applied successfully to feature selection of real-valued data sets [7], [11], [14]–[18].”
• Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, methodology): “extended the entropy to measure the information quantity in fuzzy sets [28] and applied the proposed measure to calculate the uncertainty in fuzzy-rough approximation spaces [29] and reduce hybrid data [30].”
• Incomplete Multigranulation Rough Set (2010, methodology): “In the past ten years, under different conditions, several extensions of the rough-set model have been accomplished [26]–[28], which include variable precision rough-set model [60], rough-set model based on tolerance relation [13], [14], [32], [45], [47], Bayesian roughset model [46], fuzzy rough-se”
• Granular Computing: Perspectives and Challenges (2013, background): “[33] reformed Shannon information measures into relation matrix representations and extended them to the fuzzy probabilistic approximation spaces in the hope to generate a uniform formulas to calculate the information quantity of the spaces.”