Yiru Zhang

@article{zhang_3199,

title = {A survey of evidential clustering: Definitions, methods, and applications},

author = {Zuowei Zhang and Yiru ZHANG and Arnaud Martin and Weiping Ding and Zhunga LIU and Hongpeng Tian},

url = {https://www.sciencedirect.com/science/article/pii/S1566253524005141},

year  = {2025},

date = {2025-03-01},

volume = {115},

pages = {102736},

abstract = {In the realm of information fusion, clustering stands out as a common subject and is extensively applied across various fields. Evidential clustering, an increasingly popular method in the soft clustering family, derives its strength from the theory of belief functions, which enables it to effectively characterize the uncertainty and imprecision of data distributions. This survey provides a comprehensive overview of evidential clustering, detailing its theoretical foundations, methodologies, and applications. Specifically, we start by briefly recalling the theory of belief functions with its transformations into other uncertainty reasoning theories. Then, we introduce the concepts of soft data, partitions, and methods with an emphasis on data and partitioning within the theory of belief functions. Subsequently, we summarize the advancements and quantitative evaluations of existing evidential clustering methods and provide a roadmap to help in selecting an appropriate method based on specific application needs. Finally, we identify the major challenges faced in the development and application of evidential clustering, pointing out promising avenues for future research, including theoretical limitations, applicable datasets, and application domains. The survey offers a structured understanding of existing evidential clustering methods, highlighting their theoretical underpinnings, practical implementations, and future research directions. It serves as a valuable resource for researchers seeking to deepen their understanding of evidential clustering.},

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pubstate = {published},

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}

@article{zhang_3115,

title = {On Computing Evidential Centroid Through Conjunctive Combination: An Impossibility Theorem},

author = {Yiru ZHANG and Sébastien Destercke and Zuowei Zhang and Tassadit Bouadi and Arnaud Martin},

url = {https://ieeexplore.ieee.org/abstract/document/9792173},

year  = {2023},

date = {2023-06-01},

journal = {IEEE Transactions on Artificial Intelligence},

volume = {4},

number = {3},

pages = {487 - 496},

abstract = {The theory of belief functions (TBFs) is now a widespread framework to deal and reason with uncertain and imprecise information, in particular to solve information fusion and clustering problems. Combination functions (rules) and distances are essential tools common to both the clustering and information fusion problems in the context of TBF, which have generated considerable literature. Distances and combination between evidence corpus of TBF are indeed often used within various clustering and classification algorithms, however, their interplay and connections have seldom been investigated, which is the topic of this article. More precisely, we focus on the problem of aggregating evidence corpus to obtain a representative one, and we show through an impossibility theorem that in this case, there is a fundamental contradiction between the use of conjunctive combination rules on the one hand, and the use of distances on the other hand. Rather than adding new methodologies, such results are instrumental in guiding the user among the many methodologies that already exist. To illustrate the interest of our results, we discuss different cases where they are at play.},

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pubstate = {published},

tppubtype = {article}

}

@article{zhang_3118,

title = {A New Belief-Based Incomplete Pattern Unsupervised Classification Method},

author = {Zuo-Wei Zhang and Zhe Liu and Zongfang Ma and Yiru ZHANG and Hao Wang},

url = {https://ieeexplore.ieee.org/abstract/document/9314890},

year  = {2022},

date = {2022-11-01},

journal = {Ieee Transactions On Knowledge And Data Engineering},

volume = {34},

number = {11},

pages = {5084 - 5097},

abstract = {The clustering of incomplete patterns is a very challenging task because the estimations may negatively affect the distribution of real centers and thus cause uncertainty and imprecision in the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) is proposed in this paper. First, the complete patterns are grouped into a few clusters by a classical soft method like fuzzy c -means to obtain the corresponding reliable centers and thereby are partitioned into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classifies unreliable patterns and the incomplete patterns edited by the neighbors. In this way, most of the edited incomplete patterns can be submitted to specific clusters. Finally, some ambiguous patterns will be carefully repartitioned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. By doing this, a few patterns that are very difficult to classify between different specific clusters will be reasonably submitted to meta-cluster which can characterize the uncertainty and imprecision of the clusters due to missing values. The simulation results show that the BPC has the potential to deal with real datasets.},

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pubstate = {published},

tppubtype = {article}

}

@article{wang_3116,

title = {Model and data driven transient thermal system modelings for contained data centers},

author = {Yewan Wang and Yiru ZHANG and David Nortershauser and Stéphane Le Masson and Jean-Marc Menaud},

url = {https://www.sciencedirect.com/science/article/pii/S0378778821010744?casa_token=mZrNi3RiAv8AAAAA:e8_dJAc-AagwClopSsNM9FI29whck3Iqiu4luw1tDyxoz0_9FLK7Py66SQQkJOU0ZJ0SlUitdA},

year  = {2022},

date = {2022-03-01},

journal = {Energy And Buildings},

volume = {258},

pages = {111790},

abstract = {The growing cloud computing infrastructures increase the demand for energy-intensive machines, such as high-performance servers and the associated cooling system. That brings challenges to the energy management of the concerned modern buildings such as the data centers. Transient Thermal System Modeling (TTSM) is an essential solution to illustrate the instant thermal variation inside the buildings based on heat transformation prediction. TTSM can help plan, organize and optimize corresponding settings to improve energy efficiency. The adoption of row-based and rack-based structures as a cooling solution has recently emerged, especially boosted by its high efficiency. In contrast, few studies have concerned the related TTSM. In this study, we propose two frameworks exclusively targeting the TTSM of data centers that adopted row-based or rack-based cooling systems. A model-driven framework (MDF) and a data-driven framework (DDF) are developed, respectively. The MDF is expressed by Ordinary Differential Equations (ODEs), it is built based on the lumped-capacitance method under several pre-declared assumptions as a general TTSM solution to related structures. The DDF is developed as an extension of the Long-Short-Term-Memory (LSTM) method to realize multivariate I/O (MIMO) predictions, which is commonly demanded in Industrial Internet of Things (IIoT) applications. Both frameworks are evaluated experimentally on a physical cluster equipped with a high-level IIoT sensor measurement system. The guide for parameter-tuning of the model is also provided in the experiment part for use case analysing. The validation results show that both frameworks can build reliable TTSM in terms of real-time temperature predictions. Moreover, a comprehensive comparison study is conducted with valuable application suggestions based on experimental discoveries.},

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pubstate = {published},

tppubtype = {article}

}

@article{zhang_3117,

title = {Belief Combination of Classifiers for Incomplete Data},

author = {Zuowei Zhang and Songtao Ye and Yiru ZHANG and Weiping Ding and Hao Wang},

url = {https://ieeexplore.ieee.org/abstract/document/9732306},

year  = {2022},

date = {2022-03-01},

journal = {Ieee-Caa Journal Of Automatica Sinica},

volume = {9},

number = {4},

pages = {652-667},

abstract = {Data with missing values, or incomplete information, brings some challenges to the development of classification, as the incompleteness may significantly affect the performance of classifiers. In this paper, we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier (BCC) method based on the evidence theory. The proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by incompleteness. In BCC, different attributes are regarded as independent sources, and the collection of each attribute is considered as a subset. Then, multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query pattern. Finally, these sub-classification results with different weights (discounting factors) are used to provide supplementary information to jointly determine the final classes of query patterns. The weights consist of two aspects: global and local. The global weight calculated by an optimization function is employed to represent the reliability of each classifier, and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern classification. Abundant comparative experiments including seven methods on twelve datasets are executed, demonstrating the out-performance of BCC over all baseline methods in terms of accuracy, precision, recall, F1 measure, with pertinent computational costs.},

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pubstate = {published},

tppubtype = {article}

}

@article{zhang_3114,

title = {A distance for evidential preferences with application to group decision making},

author = {Yiru ZHANG and Tassadit Bouadi and Yewan Wang and Arnaud Martin},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0020025521002486},

year  = {2021},

date = {2021-08-01},

journal = {Information Sciences},

volume = {568},

pages = {113-132},

abstract = {In this paper, we focus on measuring the dissimilarity between preferences with uncertainty and imprecision, modelled by evidential preferences based on the theory of belief functions. Two issues are targeted: The first concerns the conflicting interpretations of incomparability, leading to a lack of consensus within the preference modelling community. This discord affects the value settings of dissimilarity measures between preference relations. After reviewing the state of the art, we propose to distinguish between two cases: indecisive and undecided, respectively modelled by a binary relation and union of all relations. The second concerns a flaw that becomes apparent when measuring the dissimilarity in the theory of belief functions. Existing dissimilarity functions in the theory of belief functions are not suitable for evidential preferences, because they measure the dissimilarity between preference relations as being identical. This is counter-intuitive and conflicting with almost all the related works. We propose a novel distance named Unequal Singleton Pair (USP) distance, able to discriminate specific singletons from others when measuring the dissimilarity. The advantages of USP distances are illustrated by the evidential preference aggregation and group decision-making applications. The experiments show that USP distance effectively improves the quality of decision results.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ma_3119,

title = {Credal Transfer Learning With Multi-Estimation for Missing Data},

author = {Zongfang Ma and Zhe Liu and Yiru ZHANG and Lin Song and Jihuan He},

url = {https://ieeexplore.ieee.org/abstract/document/9046814},

year  = {2020},

date = {2020-03-01},

journal = {Ieee Access},

volume = {8},

pages = {70316 - 70328},

abstract = {Transfer learning (TL) has grown popular in recent years. It is effective to improve the classification accuracy in the target domain by using the training knowledge in the related domain (called source domain). However, the classification of missing data (or incomplete data) is a challenging task for TL because different strategies of imputation may have strong impacts on learning models. To address this problem, we propose credal transfer learning (CTL) with multi-estimation for missing data based on belief function theory by introducing uncertainty and imprecision in data imputation procedure. CTL mainly consists of three steps: Firstly, the query patterns are reasonably mapped into multiple versions in source domain to characterize the uncertainty caused by missing values. Afterwards, the multiple mapping patterns are classified in the source domain to obtain the corresponding outputs with different discounting factors. Finally, the discounted outputs, represented by the basic belief assignments (BBAs), are submitted to a new belief-based fusion system to get the final classification result for the query patterns. Three comparative experiments are given to illustrate the interests and potentials of CTL method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}