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Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications / by Muhammad Summair Raza, Usman Qamar.
- Format:
- Book
- Author/Creator:
- Raza, Muhammad Summair, author.
- Qamar, Usman, author.
- Series:
- Computer Science (Springer-11645)
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Application software.
- Database management.
- Data mining.
- Numerical analysis.
- Artificial Intelligence.
- Information Systems Applications (incl. Internet).
- Database Management.
- Data Mining and Knowledge Discovery.
- Numeric Computing.
- Local Subjects:
- Artificial Intelligence.
- Information Systems Applications (incl. Internet).
- Database Management.
- Data Mining and Knowledge Discovery.
- Numeric Computing.
- Physical Description:
- 1 online resource (XIII, 194 pages) : 75 illustrations
- Edition:
- First edition 2017.
- Contained In:
- Springer eBooks
- Place of Publication:
- Singapore : Springer Singapore : Imprint: Springer, 2017.
- System Details:
- text file PDF
- Summary:
- 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.
- Contents:
- Introduction to Feature Selection
- Background
- Rough Set Theory
- Advance Concepts in RST
- Rough Set Based Feature Selection Techniques
- Unsupervised Feature Selection using RST
- Critical Analysis of Feature Selection Algorithms
- RST Source Code.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-10-4965-1
- 9789811049651
- Access Restriction:
- Restricted for use by site license.
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