Rough set-aided keyword reduction for text categorization

By Alexios Chouchoulas, Qiang Shen. Applied Artificial Intelligence, 2001

307 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/3421cf2b556f503c2cd57b2709edcce06733e90a

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. Pradipta Maji
3. Partha Garai
4. Qiang Shen
5. Jie Yang

In Computational Theory and Mathematics

1. Richard Jensen
2. Qiang Shen
3. Pradipta Maji
4. Partha Garai
5. Jie Yang
6. Qing Xu
7. Jianhua Dai
8. Qinghua Hu

In Information Systems

1. Richard Jensen
2. Qiang Shen
3. Pradipta Maji
4. Partha Garai
5. Qing Xu
6. Jianhua Dai
7. Qinghua Hu
8. Daren Yu

Citations in the corpus

• Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches (2004, background): “The QUICKREDUCT Algorithm given in Fig. 1 (adapted from [ 10 ]), attempts to calculate a reduct without exhaustively generating all possible subsets.”
• Feature selection based on rough sets and particle swarm optimization (2007, methodology): “Rough sets have been a useful feature selection method in pattern recognition (Chouchoulas and Shen, 2001).”
• Fuzzy–Rough Simultaneous Attribute Selection and Feature Extraction Algorithm (2013, background): “extensively studied and compared with that of some existing feature selection and extraction algorithms, namely, maximal-relevance (Max-Relevance) and maximal-relevance maximal-significance (MRMS) [9] frameworks with classical, neighborhood, and fuzzy-rough sets, quick reduct (MaxDependency and roug”
• Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “This will produce exactly the same path to a reduct, due to the monotonicity of the measure [54], without the computational overhead of calculating the dataset consistency.”
• New Approaches to Fuzzy-Rough Feature Selection (2009, methodology): “One such criterion could be to terminate the search when there is no further increase in the dependency measure [ 5 ].”
• Fuzzy-Rough Sets Assisted Attribute Selection (2007, methodology): “The QUICKREDUCT algorithm given in Fig. 2 (adapted from [ 7 ]), calculates reducts without exhaustively generating all possible subsets.”