Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Unifying instance-based and rule-based induction
Machine Learning
Pruning Algorithms for Rule Learning
Machine Learning
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Learning with Nested Generalized Exemplars
Learning with Nested Generalized Exemplars
A General Framework for Induction and a Study of Selective Induction
Machine Learning
Self-Organizing Cases to Find Paradigms
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
Autonomous Clustering for Machine Learning
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
A study of instance-based algorithms for supervised learning tasks: mathematical, empirical, and psychological evaluations
Simplifying decision trees: A survey
The Knowledge Engineering Review
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A Heuristic Approach to Learning Rules from Fuzzy Databases
IEEE Intelligent Systems
A new learning method for single layer neural networks based on a regularized cost function
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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Let us consider a set of training examples described by continuous or symbolic attributes with categorical classes. In this paper we present a measure of the potential quality of a region of the attribute space to be represented as a rule condition to classify unseen cases. The aim is to take into account the distribution of the classes of the examples. The resulting measure, called impurity level, is inspired by a similar measure used in the instance-based algorithm IB3 for selecting suitable paradigmatic exemplars that will classify, in a nearest-neighbor context, future cases. The features of the impurity level are illustrated using a version of Quinlan's well-known C4.5 where the information-based heuristics are replaced by our measure. The experiments carried out to test the proposals indicate a very high accuracy reached with sets of classification rules as small as those found by RIPPER.