Predicate selection for structural decision trees

被引：0

作者：

Ng, KS ^{[1
]}

Lloyd, JW ^{[1
]}

机构：

[1] Australian Natl Univ, Res Sch Informat Sci & Engn, Comp Sci Lab, Canberra, ACT, Australia

来源：

INDUCTIVE LOGIC PROGRAMMING, PROCEEDINGS | 2005年 / 3625卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in classification learning, where we reconsider the use of accuracy as a predicate selection function and show that, on practical grounds, it is a better alternative to other commonly used functions. The second is in regression learning, where we consider the standard mean squared error measure and give a predicate pruning result for it.

引用

页码：264 / 278

页数：15

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