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Xiuwu Liao
Xi'an Jiaotong University
29Publications
12H-index
584Citations
Publications 32
Newest
#1Mengzhuo GuoH-Index: 1
#2Qingpeng ZhangH-Index: 9
view all 4 authors...
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 model is learned. However, they (a) may derive contradictory explanations on the same predictions given different methods and data samples, and (b) focus on using simpler models to provide higher descriptive accuracy at the sacrifice of prediction accuracy. To address these issues, we propose a hybrid interpretable model ...
#1Mengzhuo Guo (CityU: City University of Hong Kong)H-Index: 1
#2Qingpeng Zhang (CityU: City University of Hong Kong)H-Index: 9
Last.Daniel Dajun Zeng (CAS: Chinese Academy of Sciences)
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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 individual attributes in an explicit form. However, there is a trade-off between performance and interpretability. Full complexity models are non-traceable black-box, whereas classic interpretable models are usually simplified with lower accuracy. This trade-off limits the application of state-of-the-art machine learn...
#1Jiapeng Liu (Xi'an Jiaotong University)H-Index: 4
#2Xiuwu Liao (Xi'an Jiaotong University)H-Index: 12
Last.Roman Slowifiski (PUT: Poznań University of Technology)H-Index: 69
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Abstract We propose a new approach to preference model learning for multiple criteria sorting within the regularization framework traditionally used in the statistical learning theory. It employs an additive piecewise-linear value function as a preference model, and infers the model’s parameters from the assignment examples concerning a subset of reference alternatives. As such, our approach belongs to the family of preference disaggregation approaches. We propose a new way of measuring the comp...
3 CitationsSource
#1Mengzhuo Guo (Ministry of Education)H-Index: 1
#2Xiuwu Liao (Ministry of Education)H-Index: 12
Last.Jiapeng Liu (Ministry of Education)H-Index: 4
view all 3 authors...
Abstract A new decision-aiding approach for multiple criteria sorting problems is proposed for considering the non-monotonic relationship between the preference and evaluations of the alternatives on specific criteria. The approach employs a value function as the preference model and requires the decision maker (DM) to provide assignment examples of a subset of reference alternatives as preference information. We assume that the marginal value function of a non-monotonic criterion is non-decreas...
1 CitationsSource
#1Mengzhuo Guo (CityU: City University of Hong Kong)H-Index: 1
#2Xiuwu Liao (Ministry of Education)H-Index: 12
Last.Qingpeng Zhang (CityU: City University of Hong Kong)H-Index: 9
view all 4 authors...
Abstract Multiple criteria approaches can assist the product manager to know the consumer preferences in the context of e-commerce. Consumer preference analysis explains what aspects of a product affect and how they affect a consumer’s purchasing decision. This issue plays an important role in e-commerce platforms from its relevance in marketing decisions such as advertisements, recommendations and promotions. In this regard, we propose a data-driven multiple criteria decision aiding (MCDA) appr...
1 CitationsSource
#1Qi WangH-Index: 1
#2Wei Du (RUC: Renmin University of China)H-Index: 3
Last.Xiuwu Liao (Xi'an Jiaotong University)H-Index: 12
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ABSTRACTThe emerging patent trading platforms help to ease information asymmetry and trust issues during transaction, but a proactive recommendation mechanism that intelligently helps patent buyers identify relevant patents is still absent in the literature. This study proposes a recommendation mechanism for patent trading empowered by heterogeneous information networks (HIN) that integrates various patent information such as patent trading, patent invention, patent citation, patent ontology, an...
Source
#1Jiapeng LiuH-Index: 4
#2Miłosz KadzińskiH-Index: 16
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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 functions (including linear, piecewise-linear, splined, and general monotone ones) under a unified analytical framework. Differently from the existing sorting methods that infer a preference model from crisp decision examples, where each reference alternative is assigned to a unique class, our framework allows to consider v...
#1Mengzhuo GuoH-Index: 1
Last.Jiapeng LiuH-Index: 4
view all 5 authors...
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 these problems, the ability to explain how the attributes affect the prediction is critical to users. However, most, if not all, existing ordinal regression models simplify such explanation in the form of constant coefficients for the main and interaction effects of individual attributes. Such explanation cannot characte...
Source
#1Jiapeng Liu (Xi'an Jiaotong University)H-Index: 4
#2Miłosz KadzińskiH-Index: 16
Last.Xiaoxin Mao (Xi'an Jiaotong University)
view all 4 authors...
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 input variables is a challenging task. In this paper, we present a new preference learning approach for multiple criteria sorting with potentially interacting criteria. It employs an additive piecewise-linear value function as the basic preference model, which is augmented with components for handling the interactions....
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