中文摘要：

模块化分类规则的归纳学习是机器学习领域应用较广的一类算法,已经发展形成了多个分支和派系,其中,Prism系列算法是当前该类学习算法的典型代表之一。Prism系列算法历经近20多年的发展,在多种归纳学习任务中得到了成功应用,目前已经成为决策树归纳算法的主要竞争者。本文在Prism系列算法基本框架的基础上,分别从单机算法和并发算法两个方面对Prism系列算法进行了综述,比较分析了不同算法对于多种分类问题的适应性、优缺点及相互关系,并展望了未来该类算法的发展方向。

英文摘要：

Inductive learning with modular classification rules is a kind of widely used algorithms in machine learning. It has developed in various aspects and branches, and one of its modern representatives is the Prism family of algorithms. The Prism family of algorithms has been applied in many kinds of inductive learning tasks after more than twenty years development, and it has become a major competitor to the induction of decision trees. This paper gives a survey on the Prim family of algorithms that run independently and simultaneously, respectively, based on the basic framework of Prism algorithms. The research has made a comparison study on multiple algorithms, with respect to their suitability, advantages and interrelation in multiple classification tasks. Furthermore, the perspectives of modular classification inductive learning algorithms are presented.