Automatic Design of Decision-Tree Induction Algorithms (SpringerBriefs in Computer Science)
暫譯: 自動化決策樹生成演算法設計 (SpringerBriefs in Computer Science)

Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas

  • 出版商: Springer
  • 出版日期: 2015-03-03
  • 售價: $2,600
  • 貴賓價: 9.5$2,470
  • 語言: 英文
  • 頁數: 176
  • 裝訂: Paperback
  • ISBN: 3319142305
  • ISBN-13: 9783319142302
  • 相關分類: Algorithms-data-structuresComputer-Science
  • 海外代購書籍(需單獨結帳)

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商品描述

Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics.

"Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.

商品描述(中文翻譯)

提供了一個詳細的研究,探討構成自上而下的決策樹誘導演算法的主要設計組件,包括分割標準、停止標準、剪枝以及處理缺失值的方法。儘管當今仍然使用的策略是根據數據使用「通用」的決策樹誘導演算法,作者主張偏差擬合策略對決策樹誘導所帶來的好處,其最終目標是自動生成針對特定應用領域的決策樹誘導演算法。為此,他們討論了如何通過進化計算的範式,有效地發現最適合的決策樹誘導演算法組件,以應對各種應用,這是隨著一個新興領域——超啟發式(hyper-heuristics)的出現而展開的。

《決策樹誘導演算法的自動設計》 對於機器學習和進化計算的學生及研究人員都將非常有用。