Rough set methods in feature selection and recognition

By Roman W. Swiniarski, Andrzej Skowron. Pattern Recognition Letters, 2003

838 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ab26729bb75d29f9d1ade33f52e226dcd60965e6

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. Qinghua Hu
2. Daren Yu
3. Richard Jensen
4. Y. Qian
5. Qiang Shen
6. Jinfu Liu
7. Jiye Liang
8. Zongxia Xie

In Computer Vision and Pattern Recognition

1. Qinghua Hu
2. Daren Yu
3. Zongxia Xie
4. Jinfu Liu
5. Witold Pedrycz
6. Qiang Shen
7. Kuttiannan Thangavel
8. Sheng Yang

Citations in the corpus

• Positive approximation: An accelerator for attribute reduction in rough set theory (2010, background): “Attribute reduction in rough set theory offers a systematic theoretic framework for consistency-based feature selection, which does not attempt to maximize the class separability but rather attempts to retain the discernible ability of original features for the objects from the universe [13,14,53].”
• Attribute reduction in decision-theoretic rough set models (2008, neutral): “Different algorithms, approaches and methodologies have been extensively studied [1,36,2,4,11,13,17,20,29,35,41,51].”
• Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches (2004, background): “Rough set theory [18], [37], [48], [56], [57] is an extension of conventional set theory that supports approximations in .”
• Feature Selection With Harmony Search (2012, methodology): “2193613 evaluation techniques have been developed in the literature to judge the quality of the discovered feature subsets, such as methods based on rough sets [6], [36], [44], [48], [50],”
• Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “The problem of finding a reduct of an information system has been the subject of much research [5,355].”
• Neighborhood rough set based heterogeneous feature subset selection (2008, neutral): “Rough set theory, proposed by Pawlak [24], has been proven to be an effective tool for feature selection, rule extraction and knowledge discovery from categorical data in recent years [2,8,25,28,29,32,33,44].”
• Information-preserving hybrid data reduction based on fuzzy-rough techniques (2006, background): “An extensive review about rough set based feature selection was given in (Swiniarski and Skowron, 2003).”
• Neighborhood classifiers (2008, methodology): “…spaces and introduced neighborhood relation into rough set methodology, which has shown to be a powerful tool to attribute reduction , feature selection, rule extraction and reasoning with uncertainty (Hu, Yu, & Xie, 2006; Hu, Yu, Xie, & Liu, 2006; Jensen & Shen, 2004; Swiniarski & Skowron, 2003).”
• Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, methodology): “The ability of classical rough set theory to categorical attribute reduction has been shown in other literatures [17].”
• Incomplete Multigranulation Rough Set (2010, methodology): “Rough-set methodology presents a novel paradigm to deal with uncertainty and has been applied to feature selection [18], [48], [49], knowledge reduction [9], [16], [51], rule extraction [1], [8], [35], [50], [59], uncertainty reasoning [20], [25], [31], decision evaluation [36]–[38], and granular co”
• Dimensionality reduction based on rough set theory: A review (2009, methodology): “[93] R.W. Swiniarski, A. Skowron, Rough set methods in feature selection and recognition, Pattern Recognition Letters 24 (2003) 833–849.”
• A rough set approach to feature selection based on ant colony optimization (2010, methodology): “It has been widely applied in many fields such as machine learning (Swiniarski and Skowron, 2003), data mining (Duan et al.”
• Feature selection based on rough sets and particle swarm optimization (2007, background): “shortest or minimal reducts while obtaining high quality classifiers based on the selected features (Swiniarski and Skowron, 2003).”
• Feature selection in machine learning: A new perspective (2018, background): “According to the theoretical principle, feature selection methods can be based on statistics [35-39], information theory [40-45], manifold [46-48], and rough set [49-53], and can be”
• Fuzzy probabilistic approximation spaces and their information measures (2006, methodology): “Rough set methodology presents a novel paradigm to deal with uncertainty and has been applied to feature selection [1], [2], knowledge reduction [3], [36], [38], rule extraction [4]–[6], uncertainty reasoning [7], [8] and granulation computing [9], [33], [34], [42].”
• Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008, background): “In addition to the prevailing application of MI in feature selection, Rough set theory (RST) [21] is also potentially feasible in feature selection and significantly reduce the pattern dimensionality [22].”
• Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, methodology): “Recently, Rough Set methods [45] have been shown to significantly reduce pattern dimensionality and proven to be viable data mining techniques in [46].”