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Automation & Control Systems
Javier G. Castellano, Serafin Moral-Garcia, Carlos J. Mantas, Maria D. Benitez, Joaquin Abellan
Summary: A Bayesian Network is a graphical structure with conditional probability tables that allows for calculating probabilities between different features, particularly useful in credit scoring and risk analysis.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2021)
Article
Engineering, Mechanical
Pengfei Wei, Fuchao Liu, Marcos Valdebenito, Michael Beer
Summary: Efficient propagation of imprecise probability models is achieved through the development of a new methodology framework named NIPI, focusing on the distributional probability-box model and the estimation of probabilistic moments of model responses. By integrating spatial correlation information revealed by the GPR model, NIPI estimations with high accuracy are derived, and numerical errors are treated as epistemic uncertainty.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Computer Science, Hardware & Architecture
R. Siva Subramanian, D. Prabha
Summary: Executing customer analysis in a systemic way can help enterprises understand consumer behavior and optimize their business. The Naive Bayes model is commonly used for this analysis, but its effectiveness depends on the consumer data used and can be affected by uncertain and redundant attributes. To address this issue, an ensemble attribute selection methodology is proposed in this study, which selects a steady and uncorrelated attribute set to improve the classification performance of the Naive Bayes model.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Mechanical
Matthias G. R. Faes, Marcos A. Valdebenito, David Moens, Michael Beer
Summary: This paper presents an efficient approach to bound the responses and failure probability of linear systems with combinations of epistemic and aleatory uncertainties. By leveraging the operator norm theorem, computational efficiency can be significantly improved when dealing with parametric uncertainties.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Chemistry, Analytical
Majed Alwateer, Abdulqader M. Almars, Kareem N. Areed, Mostafa A. Elhosseini, Amira Y. Haikal, Mahmoud Badawy
Summary: A novel approach for processing healthcare data is introduced in this paper to predict useful information with minimum computational cost, aiming to improve accuracy and reduce processing time. The proposed method utilizes the Whale Optimization Algorithm and Naive Bayes Classifier for data processing and feature selection, resulting in enhanced accuracy and processing speed.
Article
Computer Science, Artificial Intelligence
Huan Zhang, Liangxiao Jiang
Summary: Naive Bayes (NB) is a simple, efficient, and effective data mining algorithm. However, its performance is limited by the unrealistic attribute conditional independence assumption and unreliable conditional probability estimation. This study proposes a novel model called fine tuned attribute weighted NB (FTAWNB), which combines fine tuning with attribute weighting to enhance NB's performance by improving both the attribute conditional independence assumption and conditional probability estimation.
Article
Genetics & Heredity
Yuxin Guo, Liping Hou, Wen Zhu, Peng Wang
Summary: The study focuses on the characteristics and identification methods of hormone binding proteins, successfully establishing a prediction model HBP_NB, using high-quality dataset and feature selection algorithm to accurately identify HBPs.
FRONTIERS IN GENETICS
(2021)
Article
Computer Science, Artificial Intelligence
Huan Zhang, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li
Summary: In this study, a new model called multi-view attribute weighted naive Bayes (MAWNB) is proposed to portray data characteristics more comprehensively. By constructing two label views from raw attributes and optimizing attribute weights, MAWNB can predict class labels for test instances with high accuracy. Extensive experiments demonstrate the superiority of MAWNB compared to NB and other state-of-the-art competitors.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Rafael Blanquero, Emilio Carrizosa, Pepa Ramirez-Cobo, M. Remedios Sillero-Denamiel
Summary: The proposed sparse Naive Bayes classifier takes into account the correlation structure of features, allows for flexible selection of performance measures, and includes performance constraints for groups of higher interest. This approach leads to competitive results in terms of accuracy, sparsity, and running times for balanced datasets, while also achieving a better compromise between classification rates for different classes in unbalanced datasets.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Mathematics
Shengfeng Gan, Shiqi Shao, Long Chen, Liangjun Yu, Liangxiao Jiang
Summary: The paper introduces a single model called hidden MNB (HMNB), which creates a hidden parent for each feature to synthesize the influences of all other qualified features by adapting the method of hidden NB (HNB). A simple but effective learning algorithm is proposed and applied to text classification datasets, validating the effectiveness of HMNB in text classification.
Article
Mathematics, Interdisciplinary Applications
David Rossell
Summary: This study focuses on the frequentist properties of Bayesian and L0 model selection in high-dimensional regression. A construction is proposed to study the concentration of posterior probabilities and normalized L0 criteria on the optimal model and other subsets of the model space. The results validate the use of posterior probabilities and L0 criteria for controlling frequentist error probabilities in model selection and hypothesis tests.
Article
Computer Science, Information Systems
Huan Zhang, Liangxiao Jiang, Chaoqun Li
Summary: In this study, a novel model called A(2)WNB is proposed to address the limitation of the attribute conditional independence assumption in naive Bayes algorithm. By discovering and utilizing latent attributes beyond the original attribute space, as well as optimizing attribute weights to reduce attribute redundancy, the A(2)WNB model demonstrates superior performance in classification tasks.
SCIENCE CHINA-INFORMATION SCIENCES
(2022)
Article
Economics
Divya Aggarwal, Pitabas Mohanty
Summary: This study examines individual risk and ambiguity preferences under three different sources of imprecise information-induced ambiguity, finding individuals showing aversion toward ambiguous investment choices, particularly those induced by conflicting information.
MANAGERIAL AND DECISION ECONOMICS
(2022)
Article
Energy & Fuels
Clemens Huebler, Raimund Rolfes
Summary: The impact of climate change on wind energy is significant, affecting both energy production efficiency and the loads on wind turbines. However, the specific effects of climate change on wind turbine loads are not yet fully understood.
Article
Physics, Multidisciplinary
Shouta Sugahara, Maomi Ueno
Summary: Previous research has shown that the classification accuracies of Bayesian networks obtained by maximizing the conditional log likelihood were higher than those obtained by maximizing the marginal likelihood. However, in cases with small sample sizes and a class variable with multiple parents, the accuracies of exact learning with ML were significantly lower. Introducing an exact learning augmented naive Bayes classifier improved the situation and guaranteed similar class posterior estimation as exact learning Bayesian networks.
Article
Computer Science, Artificial Intelligence
Carlos J. Mantas, Joaquin Abellan, Javier G. Castellano
EXPERT SYSTEMS WITH APPLICATIONS
(2016)
Article
Computer Science, Artificial Intelligence
Joaquin Abelian, Javier G. Castellano
EXPERT SYSTEMS WITH APPLICATIONS
(2017)
Article
Computer Science, Theory & Methods
Joaquin Abellan, Griselda Lopez, Laura Garach, Javier G. Castellano
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Joaquin Abellan, Carlos J. Mantas, Javier G. Castellano
KNOWLEDGE-BASED SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Joaquin Abelian, Carlos J. Mantas, Javier G. Castellano
EXPERT SYSTEMS WITH APPLICATIONS
(2018)
Article
Computer Science, Artificial Intelligence
Joaquin Abellan, Carlos J. Mantas, Javier G. Castellano, SerafIn Moral-Garcia
EXPERT SYSTEMS WITH APPLICATIONS
(2018)
Article
Computer Science, Artificial Intelligence
Serafin Moral-Garcia, Carlos J. Mantas, Javier G. Castellano, Joaquin Abellan
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2018)
Article
Physics, Multidisciplinary
Serafin Moral-Garcia, Javier G. Castellano, Carlos J. Mantas, Alfonso Montella, Joaquin Abellan
Article
Biochemistry & Molecular Biology
Luis M. de Campos, Andres Cano, Javier G. Castellano, Serafin Moral
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
(2019)
Article
Computer Science, Artificial Intelligence
S. Moral-Garcia, Carlos J. Mantas, Javier G. Castellano, Maria D. Benitez, Joaquin Abellan
EXPERT SYSTEMS WITH APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Javier G. Castellano, Serafin Moral-Garcia, Carlos J. Mantas, Joaquin Abellan
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Automation & Control Systems
Javier G. Castellano, Serafin Moral-Garcia, Carlos J. Mantas, Maria D. Benitez, Joaquin Abellan
Summary: A Bayesian Network is a graphical structure with conditional probability tables that allows for calculating probabilities between different features, particularly useful in credit scoring and risk analysis.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Serafin Moral-Garcia, Javier G. Castellano, Carlos J. Mantas, Joaquin Abellan
Summary: The Naive Credal Classifier (NCC) was the first proposed method for Imprecise Classification. In this work, a new version of NCC called Extreme Prior Naive Credal Classifier (EP-NCC) is proposed, which considers the prior probabilities of the class variable when estimating the conditional probabilities. It is shown that EP-NCC achieves more informative predictions without increasing the risk of errors. Experimental analysis demonstrates that EP-NCC outperforms NCC and achieves statistically equivalent results to the existing algorithm for Imprecise Classification based on decision trees, while being computationally simpler.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics, Interdisciplinary Applications
Joaquin Abellan, Javier G. Castellano, Carlos J. Mantas