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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Using Ant Programming Guided by Grammar for Building Rule-Based Classifiers
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Using Ant Programming Guided by Grammar for Building Rule-Based Classifiers

机译：使用语法指导的Ant编程来构建基于规则的分类器

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摘要

The extraction of comprehensible knowledge is one of the major challenges in many domains. In this paper, an ant programming (AP) framework, which is capable of mining classification rules easily comprehensible by humans, and, therefore, capable of supporting expert-domain decisions, is presented. The algorithm proposed, called grammar based ant programming (GBAP), is the first AP algorithm developed for the extraction of classification rules, and it is guided by a context-free grammar that ensures the creation of new valid individuals. To compute the transition probability of each available movement, this new model introduces the use of two complementary heuristic functions, instead of just one, as typical ant-based algorithms do. The selection of a consequent for each rule mined and the selection of the rules that make up the classifier are based on the use of a niching approach. The performance of GBAP is compared against other classification techniques on 18 varied data sets. Experimental results show that our approach produces comprehensible rules and competitive or better accuracy values than those achieved by the other classification algorithms compared with it.
机译：可理解知识的提取是许多领域的主要挑战之一。在本文中，提出了一种蚂蚁编程（AP）框架，该框架能够挖掘人类容易理解的分类规则，因此能够支持专家域决策。提出的称为基于语法的蚂蚁编程（GBAP）的算法是为提取分类规则而开发的第一个AP算法，并且以确保创建新的有效个体的无上下文语法为指导。为了计算每个可用运动的过渡概率，此新模型引入了两个互补启发式函数的使用，而不是像典型的基于蚂蚁的算法那样仅使用一个。对每个挖掘规则的结果选择和构成分类器的规则的选择均基于适当的方法。在18种不同的数据集上，GBAP的性能与其他分类技术进行了比较。实验结果表明，与其他分类算法相比，与其他分类算法相比，我们的方法可产生可理解的规则和具有竞争力的或更好的精度值。

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