Authors:
Rebeca Méndez
;
Beatriz Remeseiro
;
Diego Peteiro-Barral
and
Manuel G. Penedo
Affiliation:
Universidade da Coruña, Spain
Keyword(s):
Tear Film Lipid Layer, Class Binarization Techniques, Feature Selection, Filters, Multiple Criteria Decision Making, Multilayer Perceptron.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
Dry eye is an increasingly popular syndrome in modern society which can be diagnosed through an automatic technique for tear film lipid layer classification. Previous studies related to this multi-class problem lack of analysis focus on class binarization techniques, feature selection and artificial neural networks. Also, all of them just use the accuracy of the machine learning algorithms as performance measure. This paper presents a methodology to evaluate different performance measures over these unexplored areas using the multiple criteria decision making method called TOPSIS. The results obtained demonstrate the effectiveness of the methodology proposed in this research.