Parametric and non-parametric feature selection for kidney transplants

Authors:
Raimundo Garcia-del-Moral;Alberto Guillén;Luis Javier Herrera;Antonio Cañas;Ignacio Rojas
Affiliations:
Intensive Care Unit. Santa Ana Hospital, Motril, Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Year:
2013

Citing 4
Cited 0

Finding the embedding dimension and variable dependencies in time series

Neural Computation
Parallel multiobjective memetic RBFNNs design and feature selection for function approximation problems

Neurocomputing
Minimising the delta test for variable selection in regression problems

International Journal of High Performance Systems Architecture
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a comparison of several methods of measuring the quality of a subset of features that characterise kidney's graft so they can be evaluated to be transplanted. First, two non-parametric methods, Delta Test and Mutual Information, are used isolated and in a multiobjective manner using a genetic algorithm and comparing the solutions will all the possible solutions obtained by brute force. Afterwards, LSSVM are used to approximate the score of the graft so, for smaller approximation errors, the subset of features is considered better. The results obtained are confirmed from the clinical perspective by an expert.