R Jensen, Q Shen - IEEE Transactions on knowledge and data …, 2004 - ieeexplore.ieee.org
Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas …
Feature selection is a challenging problem in areas such as pattern recognition, machine learning and data mining. Considering a consistency measure introduced in rough set …
Q Hu, D Yu, J Liu, C Wu - Information sciences, 2008 - Elsevier
Feature subset selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Most of researches are focused on dealing …
J Dai, H Hu, WZ Wu, Y Qian… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Attribute reduction is one of the biggest challenges encountered in computational intelligence, data mining, pattern recognition, and machine learning. Effective in feature …
Tumor classification based on gene expression levels is important for tumor diagnosis. Since tumor data in gene expression contain thousands of attributes, attribute selection for …
Q Hu, D Yu, Z Xie - Pattern recognition letters, 2006 - Elsevier
Data reduction plays an important role in machine learning and pattern recognition with a high-dimensional data. In real-world applications data usually exists with hybrid formats, and …
R Jensen, Q Shen - IEEE Transactions on fuzzy systems, 2007 - ieeexplore.ieee.org
Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as …
K Thangavel, A Pethalakshmi - Applied soft computing, 2009 - Elsevier
A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of decision system and it has been applied successfully in all the fields. It is used to identify the …
The rough and fuzzy set approaches presented here open up many new frontiers for continued research and development Computational Intelligence and Feature Selection …