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Ordinal regression methods: survey and experimental study
(2017-02-03)
Abstract—Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a
categorical scale which shows a natural order between the labels. Many real-world applications ...
An ordinal CNN approach for the assessment of neurological damage in Parkinson’s disease patients
(Elsevier, 2021)
3D image scans are an assessment tool for neurological damage in Parkinson’s disease (PD) patients. This diagnosis process can be automatized to help medical staff through Decision Support Systems (DSSs), and Convolutional ...
Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching?
(MDPI, 2022)
Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor–recipient matching have become necessary. Most of the currently proposed ...
An Extended Approach of a Two-Stage Evolutionary Algorithm in Artificial Neural Networks for Multiclassification Tasks
(Springer, 2016)
This chapter considers a recent algorithm to add broader diversity at the beginning of the evolutionary process and extends it to sigmoidal neural networks. A simultaneous evolution of architectures and weights is performed ...
A two-stage algorithm in evolutionary product unit neural networks for classification
(Elsevier, 2011)
This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of ...
Memetic Evolutionary Multi-Objective Neural Network Classifier to Predict Graft Survival in Liver Transplant Patients
(2017-02-17)
In liver transplantation, matching donor and recipient is a
problem that can be solved using machine learning techniques.
In this paper we consider a liver transplant dataset
obtained from eleven Spanish hospitals, ...