Topic
C4.5 algorithm
About: C4.5 algorithm is a research topic. Over the lifetime, 1314 publications have been published within this topic receiving 39194 citations.
Papers published on a yearly basis
Papers
Book•
15 Oct 1992TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which 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.
Abstract: 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.
27,206 citations
IBM1
TL;DR: Issues in building a scalable classifier are discussed and the design of SLIQ, a new classifier that uses a novel pre-sorting technique in the tree-growth phase to enable classification of disk-resident datasets is presented.
Abstract: Classification is an important problem in the emerging field of data mining Although classification has been studied extensively in the past, most of the classification algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets This paper discusses issues in building a scalable classifier and presents the design of SLIQ, a new classifier SLIQ is a decision tree classifier that can handle both numeric and categorical attributes It uses a novel pre-sorting technique in the tree-growth phase This sorting procedure is integrated with a breadth-first tree growing strategy to enable classification of disk-resident datasets SLIQ also uses a new tree-pruning algorithm that is inexpensive, and results in compact and accurate trees The combination of these techniques enables SLIQ to scale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining
924 citations
TL;DR: An incremental algorithm for inducing decision trees equivalent to those formed by Quinlan's nonincremental ID3 algorithm, given the same training instances is presented, named ID5R.
Abstract: This article presents an incremental algorithm for inducing decision trees equivalent to those formed by Quinlan's nonincremental ID3 algorithm, given the same training instances. The new algorithm, named ID5R, lets one apply the ID3 induction process to learning tasks in which training instances are presented serially. Although the basic tree-building algorithms differ only in how the decision trees are constructed, experiments show that incremental training makes it possible to select training instances more carefully, which can result in smaller decision trees. The ID3 algorithm and its variants are compared in terms of theoretical complexity and empirical behavior.
866 citations
TL;DR: A new hybrid model can be used to estimate the intrusion scope threshold degree based on the network transaction data’s optimal features that were made available for training and revealed that the hybrid approach had a significant effect on the minimisation of the computational and time complexity involved when determining the feature association impact scale.
660 citations
TL;DR: In this article, the basic concepts of a multistage classification strategy called the decision tree classifier are presented and two methods for designing decision trees are discussed and experimental results are reported.
Abstract: This paper presents the basic concepts of a multistage classification strategy called the decision tree classifier. Two methods for designing decision trees are discussed and experimental results are reported. The relative advantages and disadvantages of each design method are considered. A spectrum of typical applications in remote sensing is noted.
490 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 19 |
| 2024 | 57 |
| 2023 | 117 |
| 2022 | 264 |
| 2021 | 94 |
| 2020 | 123 |