An Overview of Inductive Learning Algorithms
An Overview of Inductive Learning Algorithms
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归纳学习算法概述
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Aksoy
中科院分区：
文献类型：
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作者：
A. Almana;M. Aksoy
学习使系统能够识别先前知识或训练数据中的模式和规律，并从中提取出一般规则。在文献中，归纳学习方法和技巧主要分为两大类。分而治之算法也称为决策树算法和分而治算法称为覆盖算法。本文首先简要介绍了决策树的概念，然后回顾了现有的知名决策树算法，包括ID3、C4.5和CART算法的描述。覆盖算法的一个众所周知的例子是规则抽取系统（RULES）家族。本文对规则算法和规则提取器-1算法的最新进展进行了概述，并对它们的优点和不足进行了解释和讨论。最后介绍了归纳学习的几个应用领域。
learning enables the system to recognize patterns and regularities in previous knowledge or training data and extract the general rules from them. In literature there are proposed two main categories of inductive learning methods and techniques. Divide-and-Conquer algorithms also called decision Tree algorithms and Separate-and-Conquer algorithms known as covering algorithms. This paper first briefly describe the concept of decision trees followed by a review of the well known existing decision tree algorithms including description of ID3, C4.5 and CART algorithms. A well known example of covering algorithms is RULe Extraction System (RULES) family. An up to date overview of RULES algorithms, and Rule Extractor-1 algorithm, their solidity as well as shortage are explained and discussed. Finally few application domains of inductive learning are presented.