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Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria
This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria.
An interpretable neural network model through piecewise linear approximation
To address these issues, we propose a hybrid interpretable model that combines a piecewise linear component and a nonlinear component.
Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation
Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis.
A hybrid machine learning framework for analyzing human decision making through learning preferences
To meet the decision maker's demand for more interpretable machine learning models, we propose a novel hybrid method, namely Neural Network-based Multiple Criteria Decision Aiding, which combines an additive value model and a fully-connected multilayer perceptron (MLP) to achieve good performance while capturing the explicit relationships between individual attributes and the prediction.
