Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

The rough and fuzzy set approaches presented here open up many new frontiers for continued research and development

Computational Intelligence and Feature Selection provides readers with the background and fundamental ideas behind Feature Selection (FS), with an emphasis on techniques based on rough and fuzzy sets. For readers who are less familiar with the subject, the book begins with an introduction to fuzzy set theory and fuzzy-rough set theory. Building on this foundation, the book provides:

• A critical review of FS methods, with particular emphasis on their current limitations

• Program files implementing major algorithms, together with the necessary instructions and datasets, available on a related Web site

• Coverage of the background and fundamental ideas behind FS

• A systematic presentation of the leading methods reviewed in a consistent algorithmic framework

• Real-world applications with worked examples that illustrate the power and efficacy of the FS approaches covered

• An investigation of the associated areas of FS, including rule induction and clustering methods using hybridizations of fuzzy and rough set theories

Computational Intelligence and Feature Selection is an ideal resource for advanced undergraduates, postgraduates, researchers, and professional engineers. However, its straightforward presentation of the underlying concepts makes the book meaningful to specialists and nonspecialists alike.

《計算智能與特徵選擇》這本書介紹了粗糙集和模糊集方法，為持續的研究和開發開拓了許多新的領域。

《計算智能與特徵選擇》為讀者提供了特徵選擇（FS）背後的背景和基本概念，重點介紹了基於粗糙集和模糊集的技術。對於對這個主題不太熟悉的讀者，本書從模糊集理論和模糊-粗糙集理論的介紹開始。在此基礎上，本書提供了以下內容：

- 對FS方法的評論，特別強調它們目前的限制
- 在相關網站上提供了實現主要算法所需的程式檔案、指令和數據集
- 背景和FS的基本概念
- 在一致的算法框架中系統地介紹了主要方法
- 通過實例展示了所介紹的FS方法的威力和效果
- 探討了與FS相關的領域，包括使用模糊和粗糙集理論混合的規則歸納和聚類方法

《計算智能與特徵選擇》是高年級本科生、研究生、研究人員和專業工程師的理想資源。然而，它對基本概念的直觀呈現使得這本書對專業人士和非專業人士都有意義。