Instance-Based Learning Algorithms
Machine Learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Selecting typical instances in instance-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A Fast Algorithm for the Nearest-Neighbor Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Representing the behaviour of supervised classification learning algorithms by Bayesian networks
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Machine Learning
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Artificial Intelligence in Medicine
Analysis of new variable selection methods for discriminant analysis
Computational Statistics & Data Analysis
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
Different metaheuristic strategies to solve the feature selection problem
Pattern Recognition Letters
A scalable approach to simultaneous evolutionary instance and feature selection
Information Sciences: an International Journal
Bi-objective feature selection for discriminant analysis in two-class classification
Knowledge-Based Systems
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The Transjugular Intrahepatic Portosystemic Shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staff's experience, the consequences of TIPS are not homogeneous for all the patients and a subgroup dies in the first six months after TIPS placement. An investigation for predicting the conduct of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. We have applied a new Estimation of Distribution Algorithms based approach in order to perform a Prototype and Feature Subset Selection to improve the classification accuracy obtained using all the variables and all the cases. Used paradigms are K-Nearest Neighbours, Artificial Neural Networks and Classification Trees.