Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
暫譯: 理解與應用粗集基礎特徵選擇：概念、技術與應用
Raza, Muhammad Summair, Qamar, Usman
- 出版商: Springer
- 出版日期: 2019-09-04
- 售價: $5,640
- 貴賓價: 9.5 折 $5,358
- 語言: 英文
- 頁數: 236
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9813291656
- ISBN-13: 9789813291652
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商品描述
This book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. 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.
The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book.
This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.
商品描述(中文翻譯)
這本書提供了基於粗集的特徵選擇的全面介紹。粗集理論最早由 Zdzislaw Pawlak 在 1982 年提出，並持續演變。該理論關注於不精確或不確定信息和知識的分類與分析，已成為數據分析的重要工具，使讀者能夠系統地研究粗集理論（RST）中的所有主題，包括基礎知識、高級概念以及使用 RST 的特徵選擇。書中還附有一個基於 RST 的 API 庫，可用於實現幾個 RST 概念和基於 RST 的特徵選擇算法。
這本書為在特徵選擇、知識發現和不確定性推理領域工作的學生、研究人員和開發者提供了重要的參考指南，特別是那些從事 RST 和粒度計算的工作者。本書的主要讀者是使用粗集理論（RST）在各個領域的大型數據集上進行特徵選擇（FS）的研究社群。然而，任何對特徵選擇感興趣的社群，如醫療、銀行和金融等，也能從本書中受益。
本書的第二版還涵蓋了基於優勢的粗集方法和模糊粗集。基於優勢的粗集方法（DRSA）是傳統粗集方法的擴展，並支持使用優勢原則的偏好順序。反過來，模糊粗集是粗集的模糊泛化。第二版的書中還提供了 DRSA 的 API 庫。
作者簡介
Dr. Muhammad Summair Raza holds a Ph.D. specialization in Software Engineering from the National University of Science and Technology (NUST), Pakistan. He completed his M.S. at the International Islamic University, Pakistan, in 2009. He is also associated with the Virtual University of Pakistan as an Assistant Professor. Having published various papers in international-level journals and conference proceedings, his research interests include Feature Selection, Rough Set Theory and Trend Analysis.
Dr. Usman Qamar has over 15 years of experience in data engineering in both academia and industry. He holds a Master's in Computer Systems Design from the University of Manchester Institute of Science and Technology (UMIST), UK, as well as an M.Phil. and Ph.D. in Computer Science from the University of Manchester, UK. Dr Qamar's research expertise is in Data and Text Mining, Expert Systems, Knowledge Discovery, and Feature Selection, areas in which he has published extensively. He is currently a Tenured Associate Professor at the Department of Computer & Software Engineering, National University of Sciences and Technology (NUST), Pakistan, where he also heads the Knowledge and Data Engineering Research Centre (KDRC).
作者簡介(中文翻譯)
穆罕默德·蘇梅爾·拉扎博士擁有巴基斯坦國立科技大學（NUST）軟體工程的博士學位。他於2009年在巴基斯坦國際伊斯蘭大學完成碩士學位。他同時也是巴基斯坦虛擬大學的助理教授。拉扎博士在國際期刊和會議論文集中發表了多篇論文，他的研究興趣包括特徵選擇、粗集理論和趨勢分析。
烏斯曼·卡馬爾博士在學術界和產業界擁有超過15年的數據工程經驗。他擁有英國曼徹斯特科技大學（UMIST）計算機系統設計的碩士學位，以及英國曼徹斯特大學的哲學碩士和博士學位。卡馬爾博士的研究專長包括數據和文本挖掘、專家系統、知識發現和特徵選擇，並在這些領域發表了大量的研究成果。他目前是巴基斯坦國立科技大學（NUST）計算機與軟體工程系的終身副教授，並擔任知識與數據工程研究中心（KDRC）的負責人。
