Multi-criteria Evaluation of Class Binarization and Feature Selection in Tear Film Lipid Layer Classification

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.

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