Comparative Analysis of Serial Decision Tree Classification Algorithms
Comparative Analysis of Serial Decision Tree Classification Algorithms
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发表时间：
2009
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通讯作者：
Matthew N. Anyanwu;S. Shiva;manyanwu
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作者：
Matthew N. Anyanwu;S. Shiva;manyanwu
基于对象的预定义知识的数据对象分类是一种数据挖掘和知识管理技术，用于将相似的数据对象分组在一起。它可以被定义为监督学习算法，因为它根据数据项与预定义类标签之间的关系为数据对象分配类标签。分类算法具有广泛的应用，例如流失预测、欺诈检测、人工智能和信用卡评级等。文献中也有许多分类算法，但决策树是最常用的，因为与其他分类算法相比，它易于实现且更易于理解。决策树分类算法可以基于数据量、计算机资源上可用的存储空间和算法的可扩展性以串行或并行方式实现。在本文中，我们将回顾决策树算法的串行实现，并确定常用的算法。我们还将使用基于样本数据记录（Statlog数据集）的实验分析来评估常用串行决策树算法的性能。
Classification of data objects based on a predefined knowledge of the objects is a data mining and knowledge management technique used in grouping similar data objects together. It can be defined as supervised learning algorithms as it assigns class labels to data objects based on the relationship between the data items with a pre-defined class label. Classification algorithms have a wide range of applications like churn pre-diction, fraud detection, artificial intelligence, and credit card rating etc. Also there are many classification algorithms available in literature but decision trees is the most commonly used because of its ease of implementation and easier to understand compared to other classification algorithms. Decision Tree classification algorithm can be implemented in a serial or parallel fashion based on the volume of data, memory space available on the computer resource and scalability of the algorithm. In this paper we will review the serial implementations of the decision tree algorithms, identify those that are commonly used. We will also use experimental analysis based on sample data records (Statlog data sets) to evaluate the performance of the commonly used serial decision tree algorithms.