Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications - Tapa dura

Acerca del autor

Dr Summair Raza has PhD specialization in Software Engineering from National University of Science and Technology (NUST), Pakistan. He completed his MS from International Islamic University, Pakistan in 2009. He is also associated with Virtual University of Pakistan as Assistant Professor. He has published various papers in international level journals and conferences. His research interests include Feature Selection, Rough Set Theory, Trend Analysis, Software Architecture, Software Design and Non-Functional Requirements.

Dr Usman Qamar has over 15 years of experience in data engineering both in academia and industry. He has Masters in Computer Systems Design from University of Manchester Institute of Science and Technology (UMIST), UK. His MPhil and PhD in Computer Science are from University of Manchester. Dr Qamar’s research expertise are in Data and Text Mining, Expert Systems, Knowledge Discovery and Feature Selection. He has published extensively in these subject areas. His Post PhD work at University of Manchester, involved various data engineering projects which included hybrid mechanisms for statistical disclosure and customer profile analysis for shopping with the University of Ghent, Belgium. He is currently an Assistant Professor at Department of Computer Engineering, National University of Sciences and Technology (NUST), Pakistan and also heads the Knowledge and Data Engineering Research Centre (KDRC) at NUST.

De la contraportada

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.