7 citations · 13 across the 4 of their papers we have counts for
5 papers
An interpretable neural network model through piecewise linear approximation
Mengzhuo Guo, Qingpeng Zhang, Xiuwu Liao +1
Most existing interpretable methods explain a black-box model in a post-hoc manner, which uses simpler models or data analysis techniques to interpret the predictions after the mod…
Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation
Mengzhuo Guo, Zhongzhi Xu, Qingpeng Zhang +2
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. In th…
A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples
Jiapeng Liu, Milosz Kadzinski, Xiuwu Liao +2
We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value funct…
A hybrid machine learning framework for analyzing human decision making through learning preferences
Mengzhuo Guo, Qingpeng Zhang, Xiuwu Liao +2
Machine learning has recently been widely adopted to address the managerial decision making problems, in which the decision maker needs to be able to interpret the contributions of…
Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria
Jiapeng Liu, Milosz Kadzinski, Xiuwu Liao +1
The learning of predictive models for data-driven decision support has been a prevalent topic in many fields. However, construction of models that would capture interactions among…