TY  - GEN
N2  - This book provides a comprehensive introduction to Rough Set-based feature selection. It enables the reader to systematically study all topics of Rough Set Theory (RST) including the preliminaries, advanced concepts and feature selection using RST. In addition, the book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms. Rough Set Theory, proposed in 1982 by Zdzislaw Pawlak, is an area in constant development. Focusing on the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis. Feature selection is one of the important applications of RST, and helps us select the features that provide us with the largest amount of useful information. The book offers a valuable reference guide for all students, researchers, and developers working in the areas of feature selection, knowledge discovery and reasoning with uncertainty, especially those involved in RST and granular computing.
DO  - 10.1007/978-981-10-4965-1.
DO  - doi
AB  - This book provides a comprehensive introduction to Rough Set-based feature selection. It enables the reader to systematically study all topics of Rough Set Theory (RST) including the preliminaries, advanced concepts and feature selection using RST. In addition, the book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms. Rough Set Theory, proposed in 1982 by Zdzislaw Pawlak, is an area in constant development. Focusing on the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis. Feature selection is one of the important applications of RST, and helps us select the features that provide us with the largest amount of useful information. The book offers a valuable reference guide for all students, researchers, and developers working in the areas of feature selection, knowledge discovery and reasoning with uncertainty, especially those involved in RST and granular computing.
T1  - Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications /
AU  - Raza, Muhammad Summair.,
AU  - Qamar, Usman.,
CN  - Q334-342
CN  - TJ210.2-211.495
ID  - 946026
KW  - Artificial intelligence.
KW  - Computer science.
KW  - Data mining.
KW  - Database management.
KW  - Numerical analysis.
SN  - 9789811049651
SN  - 9811049653
SN  - 9789811049644
SN  - 9811049645
TI  - Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications /
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-4965-1
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-4965-1
ER  -