一种归纳算法的研究与改进

Research and improvement of an inductive algorithm

机器学习是人工智能应用的一个重要研究领域,分类算法是数据挖掘中的一种重要技术,决策树学习是其中常用的一种方法,本文针对目前在机器学习和数据挖掘领域中普遍采用的典型的决策树分类算法ID3算法存在的缺点提出了一种基于统计理论的统计归纳SIA分类算法,该算法以概率统计学的理论为基础,利用实例中提供给人们的统计规律的信息进行划分归类,从而简化了决策树的剪枝和优化过程,并通过Delphi实现该算法进行仿真试验,具有准确性高,分类速度快的特点。

Machine learning is an important domain in applications of artificial intelligence. Decision tree learning is a commonly used classification algorithm, which is an important technology in data mining. We analyzed disadvantages of ID3 algorithm,one of decision tree classification algorithm, which is widely used in machine learning and data mining. Hence, we put forward a statistical SIA classification algorithm based on statistical theory,in which the information from examples is classified to simplify the decision tree pruning and optimization process. Simulation test by Delphi shows that this approach has high accuracy and fast classification speed.