Open AccessPosted Content
Rule-based Machine Learning Methods for Functional Prediction
Sholom M. Weiss,Nitin Indurkhya +1 more
TLDR
In this article, a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables, is described, which induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules.Abstract:
We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.read more
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An Extensive Checklist for Building AutoML Systems.
TL;DR: An analysis of the components and technologies in the domains of autoML, hyperparameter tuning and meta learning and, presents a checklist of steps to follow while building an AutoML system will assist in developing a novel architecture.
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Understanding the Ability of Deep Neural Networks to Count Connected Components in Images
Shuyue Guan,Murray H. Loew +1 more
TL;DR: In this article, the authors studied the subitizing ability of deep neural networks to count the number of objects in an amount of time that increases slowly with the size of the objects and found that DNNs do not have the ability to generally count connected components.
References
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Journal ArticleDOI
Simplifying decision trees
TL;DR: Techniques for simplifying decision trees while retaining their accuracy are discussed, described, illustrated, and compared on a test-bed of decision trees from a variety of domains.
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
Automated learning of decision rules for text categorization
TL;DR: It is shown that machine-generated decision rules appear comparable to human performance, while using the identical rule-based representation, and compared with other machine-learning techniques.