Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
暫譯: 理解與使用粗集基礎的特徵選擇：概念、技術與應用
Raza, Muhammad Summair, Qamar, Usman
- 出版商: Springer
- 出版日期: 2018-12-12
- 售價: $4,990
- 貴賓價: 9.5 折 $4,741
- 語言: 英文
- 頁數: 194
- 裝訂: Quality Paper - also called trade paper
- ISBN: 981135278X
- ISBN-13: 9789811352782
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相關主題
商品描述
The book will provide:
1) In depth explanation of rough set theory along with examples of the concepts.
2) Detailed discussion on idea of feature selection.
3) Details of various representative and state of the art feature selection techniques along with algorithmic explanations.4) Critical review of state of the art rough set based feature selection methods covering strength and weaknesses of each.
5) In depth investigation of various application areas using rough set based feature selection.
6) Complete Library of Rough Set APIs along with complexity analysis and detailed manual of using APIs
7) Program files of various representative Feature Selection algorithms along with explanation of each.
The book will be a complete and self-sufficient source both for primary and secondary audience. Starting from basic concepts to state-of-the art implementation, it will be a constant source of help both for practitioners and researchers.Book will provide in-depth explanation of concepts supplemented with working examples to help in practical implementation. As far as practical implementation is concerned, the researcher/practitioner can fully concentrate on his/her own work without any concern towards implementation of basic RST functionality.
Providing complexity analysis along with full working programs will further simplify analysis and comparison of algorithms.
商品描述(中文翻譯)
本書將提供：
1) 對粗集理論的深入解釋，並附有概念的範例。
2) 對特徵選擇概念的詳細討論。
3) 各種代表性及最先進的特徵選擇技術的詳細說明，並附有演算法解釋。
4) 對最先進的基於粗集的特徵選擇方法的批判性回顧，涵蓋每種方法的優缺點。
5) 對使用基於粗集的特徵選擇的各種應用領域的深入調查。
6) 完整的粗集 API 庫，並附有複雜度分析及使用 API 的詳細手冊。
7) 各種代表性特徵選擇演算法的程式檔案，並附有每個演算法的解釋。
本書將成為初級和次級讀者的完整且自足的資源。從基本概念到最先進的實作，它將持續為實務工作者和研究人員提供幫助。
本書將提供深入的概念解釋，並輔以實作範例，以協助實際應用。就實際應用而言，研究者/實務工作者可以完全專注於自己的工作，而無需擔心基本 RST 功能的實作。
提供複雜度分析以及完整的工作程式將進一步簡化演算法的分析和比較。
作者簡介
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.
作者簡介(中文翻譯)
Dr. Summair Raza 擁有巴基斯坦國立科技大學 (NUST) 的軟體工程博士學位。他於2009年在巴基斯坦國際伊斯蘭大學完成碩士學位。他同時擔任巴基斯坦虛擬大學的助理教授。他在國際期刊和會議上發表了多篇論文。他的研究興趣包括特徵選擇、粗集理論、趨勢分析、軟體架構、軟體設計和非功能性需求。
Dr. Usman Qamar 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) 的計算機系統設計碩士學位。他的碩士研究 (MPhil) 和博士學位均來自曼徹斯特大學。Dr. Qamar 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。他在這些主題領域發表了大量的研究成果。他在曼徹斯特大學的博士後工作涉及多個數據工程項目，包括為比利時根特大學的統計披露和顧客檔案分析設計的混合機制。目前，他是巴基斯坦國立科技大學 (NUST) 計算機工程系的助理教授，並且擔任知識與數據工程研究中心 (KDRC) 的負責人。
