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Journal articles on the topic 'Probabilistic Graphical Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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2

Höhna, Sebastian, Tracy A. Heath, Bastien Boussau, Michael J. Landis, Fredrik Ronquist, and John P. Huelsenbeck. "Probabilistic Graphical Model Representation in Phylogenetics." Systematic Biology 63, no. 5 (June 20, 2014): 753–71. http://dx.doi.org/10.1093/sysbio/syu039.

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3

Javidian, Mohammad Ali, Zhiyu Wang, Linyuan Lu, and Marco Valtorta. "On a hypergraph probabilistic graphical model." Annals of Mathematics and Artificial Intelligence 88, no. 9 (July 10, 2020): 1003–33. http://dx.doi.org/10.1007/s10472-020-09701-7.

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Denev, Alexander, Adrien Papaioannou, and Orazio Angelini. "A probabilistic graphical models approach to model interconnectedness." International Journal of Risk Assessment and Management 23, no. 2 (2020): 119. http://dx.doi.org/10.1504/ijram.2020.10028855.

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Denev, Alexander, Adrien Papaioannou, and Orazio Angelini. "A probabilistic graphical models approach to model interconnectedness." International Journal of Risk Assessment and Management 23, no. 2 (2020): 119. http://dx.doi.org/10.1504/ijram.2020.106963.

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Ahn, Gil Seung, and Sun Hur. "Probabilistic Graphical Model for Transaction Data Analysis." Journal of Korean Institute of Industrial Engineers 42, no. 4 (August 15, 2016): 249–55. http://dx.doi.org/10.7232/jkiie.2016.42.4.249.

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7

Wan, Jiang, and Nicholas Zabaras. "A probabilistic graphical model based stochastic input model construction." Journal of Computational Physics 272 (September 2014): 664–85. http://dx.doi.org/10.1016/j.jcp.2014.05.002.

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8

KRAUSE, PAUL J. "Learning probabilistic networks." Knowledge Engineering Review 13, no. 4 (February 1999): 321–51. http://dx.doi.org/10.1017/s0269888998004019.

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A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered. In order to make the paper as self contained as possible, we start with an introduction to probability theory and probabilistic graphical models. The paper concludes with a short discussion on how these techniques can be applied to the problem of learning causal relationships between variables in a domain of interest.

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Murray, Richard F. "A probabilistic graphical model of lightness and lighting." Journal of Vision 19, no. 10 (September 6, 2019): 298a. http://dx.doi.org/10.1167/19.10.298a.

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Zhang, Mingjie, and Baosheng Kang. "Visual Tracking Algorithm Based on Probabilistic Graphical Model." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 9 (September 30, 2015): 157–66. http://dx.doi.org/10.14257/ijsip.2015.8.9.16.

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Zhang, Mingjin, Nannan Wang, Yunsong Li, and Xinbo Gao. "Neural Probabilistic Graphical Model for Face Sketch Synthesis." IEEE Transactions on Neural Networks and Learning Systems 31, no. 7 (July 2020): 2623–37. http://dx.doi.org/10.1109/tnnls.2019.2933590.

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12

Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (January 2, 2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.

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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.

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Martinez, Oscar, Ranga Dabarera, Kamal Premaratne, and Miroslav Kubat. "LDA-based probabilistic graphical model for excitation-emission matrices." Intelligent Data Analysis 19, no. 5 (September 8, 2015): 1109–30. http://dx.doi.org/10.3233/ida-150761.

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14

Nikolaevna, Palamar Irina, and Sergey Sergeevich Yulin. "Generative Probabilistic Graphical Model Base on the Principal Manifolds." SPIIRAS Proceedings 2, no. 33 (May 15, 2014): 227. http://dx.doi.org/10.15622/sp.33.12.

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15

Han, Zhongming, Ke Yang, Fengmin Xu, and Dagao Duan. "Probabilistic graphical model for detecting spammers in microblog websites." International Journal of Embedded Systems 8, no. 1 (2016): 12. http://dx.doi.org/10.1504/ijes.2016.073747.

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16

Lee, Wonjung, and Nicholas Zabaras. "Parallel probabilistic graphical model approach for nonparametric Bayesian inference." Journal of Computational Physics 372 (November 2018): 546–63. http://dx.doi.org/10.1016/j.jcp.2018.06.057.

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17

Badrinarayanan, Vijay, Ignas Budvytis, and Roberto Cipolla. "Mixture of Trees Probabilistic Graphical Model for Video Segmentation." International Journal of Computer Vision 110, no. 1 (December 13, 2013): 14–29. http://dx.doi.org/10.1007/s11263-013-0673-5.

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18

Acharya, Chiranjit, and Sankar K. Pal. "Probabilistic Graphical Model based Approach to Genetic Algorithm Design." IETE Journal of Research 48, no. 5 (September 2002): 339–47. http://dx.doi.org/10.1080/03772063.2002.11416295.

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19

Xu, Feng, Chenrong Huang, Zhengjun Wu, and Lizhong Xu. "Video multi-target tracking based on probabilistic graphical model." Journal of Electronics (China) 28, no. 4-6 (November 2011): 548–57. http://dx.doi.org/10.1007/s11767-012-0754-6.

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20

Liu, Wei-Yi, Kun Yue, and Ming-Hai Gao. "Constructing probabilistic graphical model from predicate formulas for fusing logical and probabilistic knowledge." Information Sciences 181, no. 18 (September 2011): 3828–45. http://dx.doi.org/10.1016/j.ins.2011.05.006.

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21

Choi, Woo-Sik, and Seoung Bum Kim. "A Novel Probabilistic Graphical Model-Based Click Model for Vertical Search." Journal of the Korean Institute of Industrial Engineers 48, no. 2 (April 15, 2022): 138–50. http://dx.doi.org/10.7232/jkiie.2022.48.2.138.

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22

Musella, Flaminia, Roberta Guglielmetti Mugion, Hendry Raharjo, and Laura Di Pietro. "Reconciling internal and external satisfaction through probabilistic graphical models." International Journal of Quality and Service Sciences 9, no. 3/4 (September 18, 2017): 347–70. http://dx.doi.org/10.1108/ijqss-02-2017-0007.

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Purpose This paper aims to holistically reconcile internal and external customer satisfaction using probabilistic graphical models. The models are useful not only in the identification of the most sensitive factors for the creation of both internal and external customer satisfaction but also in the generation of improvement scenarios in a probabilistic way. Design/methodology/approach Standard Bayesian networks and object-oriented Bayesian networks are used to build probabilistic graphical models for internal and external customers. For each ward, the model is used to evaluate satisfaction drivers by category, and scenarios for the improvement of overall satisfaction variables are developed. A global model that is based on an object-oriented network is modularly built to provide a holistic view of internal and external satisfaction. The linkage is created by building a global index of internal and external satisfaction based on a linear combination. The model parameters are derived from survey data from an Italian hospital. Findings The results that were achieved with the Bayesian networks are consistent with the results of previous research, and they were obtained by using a partial least squares path modelling tool. The variable ‘Experience’ is the most relevant internal factor for the improvement of overall patient satisfaction. To improve overall employee satisfaction, the variable ‘Product/service results’ is the most important. Finally, for a given target of overall internal and external satisfaction, external satisfaction is more sensitive to improvement than internal satisfaction. Originality/value The novelty of the paper lies in the efforts to link internal and external satisfaction based on a probabilistic expert system that can generate improvement scenarios. From an academic viewpoint, this study moves the service profit chain theory (Heskett et al., 1994) forward by delivering operational guidelines for jointly managing the factors that affect internal and external customer satisfaction in service organizations using a holistic approach.

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23

Kappen, Hilbert, Vicenç Gomez, and Manfred Opper. "Optimal Control as a Graphical Model Inference Problem." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 472–73. http://dx.doi.org/10.1609/icaps.v23i1.13573.

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In this paper we show the identification between stochastic optimal control computation and probabilistic inference on a graphical model for certain class of control problems. We refer to these problems as Kullback-Leibler (KL) control problems. We illustrate how KL control can be used to model a multi-agent cooperative game for which optimal control can be approximated using belief propagation when exact inference is unfeasible.

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24

Baltiyskiy, Igor Andreevich, and Sergey Igorevich Nikolenko. "A probabilistic graphical model for the music harmony similarity task." SPIIRAS Proceedings 3, no. 18 (March 17, 2014): 136. http://dx.doi.org/10.15622/sp.18.6.

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25

Eryilmaz, Sukru Burc, Emre Neftci, Siddharth Joshi, SangBum Kim, Matthew BrightSky, Hsiang-Lan Lung, Chung Lam, Gert Cauwenberghs, and Hon-Sum Philip Wong. "Training a Probabilistic Graphical Model With Resistive Switching Electronic Synapses." IEEE Transactions on Electron Devices 63, no. 12 (December 2016): 5004–11. http://dx.doi.org/10.1109/ted.2016.2616483.

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26

Baudrit, Cédric, Nathalie Perrot, Jean Marie Brousset, Philippe Abbal, Hervé Guillemin, Bruno Perret, Etienne Goulet, Laurence Guerin, Gérard Barbeau, and Daniel Picque. "A probabilistic graphical model for describing the grape berry maturity." Computers and Electronics in Agriculture 118 (October 2015): 124–35. http://dx.doi.org/10.1016/j.compag.2015.08.019.

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27

Krishneswari, K., and S. Arumugam. "Investigation of Probabilistic Graphical Model Algorithms for Palm print Verification." International Journal of Computer Applications 28, no. 5 (August 31, 2011): 6–9. http://dx.doi.org/10.5120/3386-4700.

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28

AlJadda, Khalifeh, Mohammed Korayem, Camilo Ortiz, Trey Grainger, John A. Miller, Khaled M. Rasheed, Krys J. Kochut, et al. "Mining massive hierarchical data using a scalable probabilistic graphical model." Information Sciences 425 (January 2018): 62–75. http://dx.doi.org/10.1016/j.ins.2017.10.014.

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29

Corso, Jason J., Guangqi Ye, and Gregory D. Hager. "Analysis of composite gestures with a coherent probabilistic graphical model." Virtual Reality 9, no. 1 (October 20, 2005): 93. http://dx.doi.org/10.1007/s10055-005-0007-1.

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30

Corso, Jason J., Guangqi Ye, and Gregory D. Hager. "Analysis of composite gestures with a coherent probabilistic graphical model." Virtual Reality 8, no. 4 (August 12, 2005): 242–52. http://dx.doi.org/10.1007/s10055-005-0157-1.

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31

Ding, Fei, and Yi Zhuang. "Ego-network probabilistic graphical model for discovering on-line communities." Applied Intelligence 48, no. 9 (February 6, 2018): 3038–52. http://dx.doi.org/10.1007/s10489-018-1137-y.

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32

Smyth, Padhraic, David Heckerman, and Michael I. Jordan. "Probabilistic Independence Networks for Hidden Markov Probability Models." Neural Computation 9, no. 2 (February 1, 1997): 227–69. http://dx.doi.org/10.1162/neco.1997.9.2.227.

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Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas, including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper presents a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.

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33

Samiullah, Md, David Albrecht, Ann E. Nicholson, and Chowdhury Farhan Ahmed. "A Review on Probabilistic Graphical Models and Tools." Dhaka University Journal of Applied Science and Engineering 6, no. 2 (June 15, 2022): 82–93. http://dx.doi.org/10.3329/dujase.v6i2.59223.

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Our daily life is full of challenges, and the biggest challenge is the unpredictability of many of our significant life events. To deal with this unpredictability, analysing the probability of events has become very important. In particular, the theorem of English statistician Thomas Bayes has been revolutionary. Numerous theories and techniques have been proposed, and many tools have been developed to solve real-life problems based on the theorem, yet it is still very much an area of active research. It still attracts researchers dealing with cutting-edge technologies. One tool that has been used extensively in modelling probabilistic analysis for decades is the Probabilistic Graphical Model (PGM). PGMs have very challenging childhood but glorious youth. The vast applicability of the models in cutting-edge technologies attracts researchers, modellers and scientists of diversified fields. Hence there are numerous models with their respective features, merits and backlogs. To date, there have been very few surveys conducted among the wide range of models and their associated tools. More specifically, those few reviews are highly application and domain focused, and limited to three to four very popular and widely used models and their associated learning and inference algorithms. To the best of our knowledge, this paper is the first that presents the features, limitations, design and implementation platforms, research challenges and applicability of the models based on a common framework that consists of some essential attributes of the popular PGMs and tools for probabilistic analysis. The study helps deciding an appropriate tool as per the perspective of the application and feature of the tool. This paper concludes with future research scope and a non-exhaustive list of applications of PGMs. DUJASE Vol. 6 (2) 82-93, 2021 (July)

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34

Baskara Nugraha, I. Gusti Bagus, Imaniar Ramadhani, and Jaka Sembiring. "Probabilistic Inference Hybrid IT Value Model Using Bayesian Network." International Journal on Electrical Engineering and Informatics 12, no. 4 (December 31, 2020): 770–85. http://dx.doi.org/10.15676/ijeei.2020.12.4.5.

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In this study, we propose probabilistic inference model on a hybrid IT value model using Bayesian Network (BN) that represents uncertain relationships between 13 variables of the model. Those variables are performance, market, innovation, IT support, core competence, capabilities, knowledge, human resources, IT development, IT resources, capital, labor, and IT spending. The relationships between variables in the model are determined using probabilistic approach, including the structure, nature, and direction of relationships. We derive a probabilistic graphical model and measure the relationships between variables. The results of this study shows that the probabilistic approach with Bayesian Network can show that capabilities and core competence are the most important variables to produce high performance output.

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35

Tellex, Stefanie, Thomas Kollar, Steven Dickerson, Matthew R. Walter, Ashis Gopal Banerjee, Seth Teller, and Nicholas Roy. "Approaching the Symbol Grounding Problem with Probabilistic Graphical Models." AI Magazine 32, no. 4 (December 16, 2011): 64–76. http://dx.doi.org/10.1609/aimag.v32i4.2384.

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n order for robots to engage in dialog with human teammates, they must have the ability to map between words in the language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as “Drive over to receiving and pick up the tire pallet.” In this article we describe several of our results that use probabilistic inference to address the symbol grounding problem. Our speciﬁc approach is to develop models that factor according to the linguistic structure of a command. We ﬁrst describe an early result, a generative model that factors according to the sequential structure of language, and then discuss our new framework, generalized grounding graphs (G3). The G3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments where the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation.

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36

Peng, Qing Song. "Construction Method to the Extension Model of Influence Diagrams." Advanced Materials Research 268-270 (July 2011): 88–90. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.88.

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Influence diagram is a kind of graphical model that can represent both the probabilistic relationship between variables and can easy to make decisions. Extension model of Influence Diagrams is reviewed in this paper and the construction method of this new model is investigated.

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37

Salek, Mahyar, Yoram Bachrach, and Peter Key. "Hotspotting — A Probabilistic Graphical Model For Image Object Localization Through Crowdsourcing." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 29, 2013): 1156–62. http://dx.doi.org/10.1609/aaai.v27i1.8465.

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Object localization is an image annotation task which consists of finding the location of a target object in an image. It is common to crowdsource annotation tasks and aggregate responses to estimate the true annotation. While for other kinds of annotations consensus is simple and powerful, it cannot be applied to object localization as effectively due to the task's rich answer space and inherent noise in responses. We propose a probabilistic graphical model to localize objects in images based on responses from the crowd. We improve upon natural aggregation methods such as the mean and the median by simultaneously estimating the difficulty level of each question and skill level of every participant. We empirically evaluate our model on crowdsourced data and show that our method outperforms simple aggregators both in estimating the true locations and in ranking participants by their ability. We also propose a simple adaptive sourcing scheme that works well for very sparse datasets.

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38

Avaylon, Matthew, Robbie Sadre, Zhe Bai, and Talita Perciano. "Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation." Advances in Artificial Intelligence and Machine Learning 02, no. 01 (2022): 288–302. http://dx.doi.org/10.54364/aaiml.2022.1119.

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Semantic segmentation algorithms based on deep learning architectures have been applied to a diverse set of problems. Consequently, new methodologies have emerged to push the state-of-the-art in this field forward, and the need for powerful user-friendly software increased significantly. The combination of Conditional Random Fields (CRFs) and Convolutional Neural Networks (CNNs) boosted the results of pixel-level classification predictions. Recent work using a fully integrated CRF-RNN layer have shown strong advantages in segmentation benchmarks over the base models. Despite this success, the rigidity of these frameworks prevents mass adaptability for complex scientific datasets and presents challenges in optimally scaling these models. In this work, we introduce a new encoder-decoder system that overcomes both these issues. We adapt multiple CNNs as encoders, allowing for the definition of multiple function parameter arguments to structure the models according to the targeted datasets and scientific problem. We leverage the flexibility of the U-Net architecture to act as a scalable decoder. The CRF-RNN layer is integrated into the decoder as an optional final layer, keeping the entire system fully compatible with back-propagation. To evaluate the performance of our implementation, we performed experiments on the Oxford-IIIT Pet Dataset and to experimental scientific data acquired via micro-computed tomography (microCT), revealing the adaptability of this framework and the performance benefits from a fully end-to-end CNN-CRF system on a both experimental and benchmark datasets.

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Musina, Valeriya Fuatovna. "Bayesian belief networks as probabilistic graphical model for medical risk assessment." SPIIRAS Proceedings 1, no. 24 (August 27, 2014): 135. http://dx.doi.org/10.15622/sp.24.9.

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40

Musina, Valeriya Fuatovna. "Bayesian belief networks as probabilistic graphical model for economical risk assessment." SPIIRAS Proceedings 2, no. 25 (March 17, 2014): 235. http://dx.doi.org/10.15622/sp.25.12.

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Benitez-Quiroz, C. F., Samuel Rivera, Paulo F. U. Gotardo, and Aleix M. Martinez. "Salient and non-salient fiducial detection using a probabilistic graphical model." Pattern Recognition 47, no. 1 (January 2014): 208–15. http://dx.doi.org/10.1016/j.patcog.2013.06.013.

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42

Kou, Feifei, Junping Du, Congxian Yang, Yansong Shi, Meiyu Liang, Zhe Xue, and Haisheng Li. "A multi-feature probabilistic graphical model for social network semantic search." Neurocomputing 336 (April 2019): 67–78. http://dx.doi.org/10.1016/j.neucom.2018.03.086.

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Liu, Weiyi, Kun Yue, Hui Liu, Ping Zhang, Suiye Liu, and Qianyi Wang. "Associative categorization of frequent patterns based on the probabilistic graphical model." Frontiers of Computer Science 8, no. 2 (March 11, 2014): 265–78. http://dx.doi.org/10.1007/s11704-014-3173-z.

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44

Zhao, Anping, and Yan Ma. "Constructing Service Semantic Link Network Based on the Probabilistic Graphical Model." International Journal of Computational Intelligence Systems 5, no. 6 (November 2012): 1040–51. http://dx.doi.org/10.1080/18756891.2012.747660.

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45

O’Neill, Zheng, and Charles O’Neill. "Development of a probabilistic graphical model for predicting building energy performance." Applied Energy 164 (February 2016): 650–58. http://dx.doi.org/10.1016/j.apenergy.2015.12.015.

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46

Wan, Jiang, and Nicholas Zabaras. "A probabilistic graphical model approach to stochastic multiscale partial differential equations." Journal of Computational Physics 250 (October 2013): 477–510. http://dx.doi.org/10.1016/j.jcp.2013.05.016.

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47

Elbahi, Anis, Mohamed Nazih Omri, Mohamed Ali Mahjoub, and Kamel Garrouch. "Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model." Arabian Journal for Science and Engineering 41, no. 8 (February 2, 2016): 2847–62. http://dx.doi.org/10.1007/s13369-016-2025-6.

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48

Trentin, Edmondo. "A Neural Probabilistic Graphical Model for Learning and Decision Making in Evolving Structured Environments." Mathematics 10, no. 15 (July 28, 2022): 2646. http://dx.doi.org/10.3390/math10152646.

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A difficult and open problem in artificial intelligence is the development of agents that can operate in complex environments which change over time. The present communication introduces the formal notions, the architecture, and the training algorithm of a machine capable of learning and decision-making in evolving structured environments. These environments are defined as sets of evolving relations among evolving entities. The proposed machine relies on a probabilistic graphical model whose time-dependent latent variables undergo a Markov assumption. The likelihood of such variables given the structured environment is estimated via a probabilistic variant of the recursive neural network.

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49

Jaakkola, T. S., and M. I. Jordan. "Variational Probabilistic Inference and the QMR-DT Network." Journal of Artificial Intelligence Research 10 (May 1, 1999): 291–322. http://dx.doi.org/10.1613/jair.583.

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We describe a variational approximation method for efficient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We describe a variational approach to the problem of diagnostic inference in the `Quick Medical Reference' (QMR) network. The QMR network is a large-scale probabilistic graphical model built on statistical and expert knowledge. Exact probabilistic inference is infeasible in this model for all but a small set of cases. We evaluate our variational inference algorithm on a large set of diagnostic test cases, comparing the algorithm to a state-of-the-art stochastic sampling method.

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50

Rodrigo, Enrique G., Juan C. Alfaro, Juan A. Aledo, and José A. Gámez. "Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem." Entropy 23, no. 4 (March 31, 2021): 420. http://dx.doi.org/10.3390/e23040420.

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Abstract:

The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements.

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