C4.5: programs for machine learning
C4.5: programs for machine learning
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Converting numerical classification into text classification
Artificial Intelligence
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
Learning from Inconsistent and Noisy Data: The AQ18 Approach
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
IEEE Transactions on Knowledge and Data Engineering
Khiops: A Statistical Discretization Method of Continuous Attributes
Machine Learning
CLIP4: hybrid inductive machine learning algorithm that generates inequality rules
Information Sciences: an International Journal - Special issue: Soft computing data mining
An Extended Chi2 Algorithm for Discretization of Real Value Attributes
IEEE Transactions on Knowledge and Data Engineering
Dual unification of bi-class support vector machine formulations
Pattern Recognition
Ent-Boost: Boosting using entropy measures for robust object detection
Pattern Recognition Letters
Support vector machines for interval discriminant analysis
Neurocomputing
Hand-Geometry Recognition Using Entropy-Based Discretization
IEEE Transactions on Information Forensics and Security
A new approach to qualitative learning in time series
Expert Systems with Applications: An International Journal
A model for the qualitative description of images based on visual and spatial features
Computer Vision and Image Understanding
Journal of Ambient Intelligence and Smart Environments
Journal of Ambient Intelligence and Smart Environments - Context Awareness
Compact classification of optimized Boolean reasoning with Particle Swarm Optimization
Intelligent Data Analysis
Hi-index | 12.05 |
This paper describes a new discretization algorithm, called Ameva, which is designed to work with supervised learning algorithms. Ameva maximizes a contingency coefficient based on Chi-square statistics and generates a potentially minimal number of discrete intervals. Its most important advantage, in contrast with several existing discretization algorithms, is that it does not need the user to indicate the number of intervals. We have compared Ameva with one of the most relevant discretization algorithms, CAIM. Tests performed comparing these two algorithms show that discrete attributes generated by the Ameva algorithm always have the lowest number of intervals, and even if the number of classes is high, the same computational complexity is maintained. A comparison between the Ameva and the genetic algorithm approaches has been also realized and there are very small differences between these iterative and combinatorial approaches, except when considering the execution time.