C4.5: Programs for Machine Learning
C4.5: Programs for Machine Learning
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发表时间：
1992-10
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通讯作者：
J. R. Quinlan
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作者：
J. R. Quinlan
来自发布者：分类器系统在机器学习和基于知识的系统中发挥着重要作用，Ross Quinlan 在 ID3 和 C4.5 方面的工作被广泛认为对其发展做出了一些最重要的贡献。本书是在 UNIX 环境下用 C 语言实现的 C4.5 系统的完整指南。它包含系统使用的全面指南、源代码（约8,800行）和实现说明。 Sun 工作站的 3.5 英寸软盘上还提供了源代码和示例数据集。 C4.5 从属于已知类别的大量案例开始。由标称和数字属性的任意混合描述的情况会被仔细检查，以寻找允许可靠地区分类别的模式。然后，这些模式以决策树或 if-then 规则集的形式表达为模型，可用于对新案例进行分类，重点是使模型易于理解且准确。该系统已成功应用于涉及数百个属性描述的数万个案例的任务。本书从简单的核心学习方法开始，展示了如何对其进行详细阐述和扩展，以处理诸如缺失数据和过度命中等典型问题。通过几个案例研究讨论并说明了 C4.5 方法的优点和缺点。基于分类的智能系统的开发人员以及机器学习和专家系统课程的学生应该会对本书和软件感兴趣。
From the Publisher: Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation. C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.