Parameter Estimation in Switching Markov Systems and Unsupervised Smoothing

Zheng, Fei; Derrode, Stéphane; Pieczynski, Wojciech

doi:10.1109/tac.2018.2863651

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…The smoothness of leaf nodes can be improved by L2 regularization to solve the overfitting problem [21,22]. In the objective function, when the complexity of the model increases, there are two different types of accumulation, one of which is I j = fijqðx i Þ = jg, where I j represents the set of samples in the leaf node j.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

A Study on RB‐XGBoost Algorithm‐Based e‐Commerce Credit Risk Assessment Model

Yang

Gao

2021

Journal of Sensors

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The current method’s e-commerce credit risk assessment is prone to poor data balance and low evaluation accuracy. An RB-XGBoost algorithm-based e-commerce credit risk assessment model is proposed in this study. The adaptive random balance (RB) method is used to sample and process the obtained data to improve the balance degree of the data. An assessment index system is constructed based on the processed data. Based on the risk evaluation index system and the XGBoost algorithm, this paper constructed an e-commerce risk assessment model and assessed the e-commerce credit risk using this model. The experimental results show that the proposed method has good data balance, a high kappa coefficient, and a large receiver operating characteristic (ROC) curve area, which can effectively improve e-commerce credit risk assessment accuracy.

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Section: Journal Of Sensorsmentioning

confidence: 99%

A Study on RB‐XGBoost Algorithm‐Based e‐Commerce Credit Risk Assessment Model

Yang

Gao

2021

Journal of Sensors

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“…In order to count agricultural data, we combine the structure of the Internet of ings. We select relevant statistical data and build a panel data system, starting from two perspectives in different regions and analyzing agricultural insurance's current development and characteristics at various stages [21]. ere are two ways of networking equipment nodes in the Internet of ings: wireless networking and wired networking; however, in some practical situations where there are longdistance transmission and complex wiring, wired networking for data transmission and remote network control is troublesome in wiring.…”

Section: Internet Of Ings Technologymentioning

confidence: 99%

Application of Markov Model-Based IoT in Agricultural Insurance and Risk Management

Chen

Jiang

2021

Mobile Information Systems

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As the foundation of the national economy, agriculture is a high-risk, weak industry. Affected by many factors, agricultural production is subject to catastrophe risks from time to time. Agricultural production is mainly faced with two major threats, natural disaster risk and market risk. As an effective risk management tool, the production and promotion of agricultural insurance have played an essential role in guaranteeing the development of the agricultural industry in some developed countries and major agricultural countries in the world. This article combines the Internet of Things and Markov model for agricultural insurance risk management. First, we combine the structure of the Internet of Things and select relevant statistical data. Then, we build a panel data system, starting from two perspectives in different regions and analyze agricultural insurance’s current development and characteristics at each stage. In addition, we use the Markov model to build a panel data model to explore the specific impact mechanisms deeply. We also study the effects of disaster risk levels in different regions on the development of agricultural insurance. After simulation verification, we believe that this model can effectively promote the balanced regional development of agricultural insurance.

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“…However, the extension of the filter to three and more classes is not as easy, with quite complex fuzzy Markov laws to deal with, but could be inspired from the work [22]. The next step in the development of an unsupervised parameter estimation method for this CGOFMSM-similar to the one proposed for the 'hard' model [23]-is the derivation of a fuzzy smoother for off-line processing. Application of the fuzzy model to deal with the design of a control system for road traffic congestion prediction in which traffic dynamics would be modeled by a switching regime model is another perspective for further work.…”

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confidence: 99%

Filtering in Gaussian Linear Systems With Fuzzy Switches

Bouyahia

Derrode

Pieczynski

2020

IEEE Trans. Fuzzy Syst.

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This work extends recent results on Conditionally Gaussian Observed Markov Switching Models (CGOMSM) by incorporating fuzzy switches in the model, instead of hard ones. This kind of generalization is of interest for applications involving continuous switching regimes, such as tracking an object using cameras in intermittent sunlight and shadow conditions. The filter developed hereby is recursive, optimal and exact, up to an approximation of integrals according to some fuzzy measure. Experiences on simulated and on real data-dealing with outdoor air temperature and power consumption of a building-confirm the accuracy and effectiveness of the proposed filter compared to the hard filter with 'crisp' switches.

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Parameter Estimation in Switching Markov Systems and Unsupervised Smoothing

Cited by 12 publications

References 31 publications

A Study on RB‐XGBoost Algorithm‐Based e‐Commerce Credit Risk Assessment Model

A Study on RB‐XGBoost Algorithm‐Based e‐Commerce Credit Risk Assessment Model

Application of Markov Model-Based IoT in Agricultural Insurance and Risk Management

Filtering in Gaussian Linear Systems With Fuzzy Switches

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