Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
TLDR
On most datasets studied, the best of very simple rules that classify examples on the basis of a single attribute is as accurate as the rules induced by the majority of machine learning systems.Abstract:
This article reports an empirical investigation of the accuracy of rules that classify examples on the basis of a single attribute. On most datasets studied, the best of these very simple rules is as accurate as the rules induced by the majority of machine learning systems. The article explores the implications of this finding for machine learning research and applications.read more
Citations
More filters
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
Book
Pattern recognition and neural networks
Brian D. Ripley,N. L. Hjort +1 more
TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.
Book
Simple Heuristics That Make Us Smart
Gerd Gigerenzer,Peter M. Todd +1 more
TL;DR: Fast and frugal heuristics as discussed by the authors are simple rules for making decisions with realistic mental resources and can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality.
Posted Content
Principles of data mining
TL;DR: This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.
References
More filters
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Journal ArticleDOI
The CN2 Induction Algorithm
Peter Clark,Tim Niblett +1 more
TL;DR: A description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present.
Journal ArticleDOI
Knowledge acquisition via incremental conceptual clustering
TL;DR: COBWEB is a conceptual clustering system that organizes data so as to maximize inference ability, and is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.
Journal ArticleDOI
Computer-Intensive Methods in Statistics
Persi Diaconis,Bradley Efron +1 more
TL;DR: The bootstrap method is examined and evaluated as an example of this new generation of statistical tools that take advantage of the high speed digital computer and free the statistician to attack more complicated problems.
Book ChapterDOI
Rule Induction with CN2: Some Recent Improvements
Peter Clark,Robin Boswell +1 more
TL;DR: Improvements to the CN2 algorithm are described, including the use of the Laplacian error estimate as an alternative evaluation function and it is shown how unordered as well as ordered rules can be generated.