Granular computing: an introduction

By Witold Pedrycz. Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2001

294 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9cfde930eaebadfe4f03a2773d3a7c5e58afcba8

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. Nitesh V. Chawla
2. Yiyu Yao
3. Sven Krasser
4. Yanqing Zhang
5. Xiaolin Hu
6. Yulong Wang
7. Jian Wu
8. Jun Zhu

In Management Science and Operations Research

1. Nitesh V. Chawla
2. Yiyu Yao
3. Sven Krasser
4. Yanqing Zhang
5. Xiaolin Hu
6. Yulong Wang
7. Jian Wu
8. Jun Zhu

Citations in the corpus

• Perspectives of granular computing (2005, methodology): “computing community formulate the problem based on theories and models of computational intelligence [3], [12], [13], [14], [15], [23], [25], [26], [30], [31], [34],”
• SVMs Modeling for Highly Imbalanced Classification (2009, result): “As opposed to traditional data-oriented numeric computing, granular computing is knowledge-oriented [24].”
• A Hierarchical Recurrent Neural Network for Symbolic Melody Generation (2017, methodology): “This method follows the general idea of granular computing (Bargiela and Pedrycz 2012), in which different resolutions of knowledge or information is extracted and represented for problem solving.”