Related references
Note: Only part of the references are listed.
Article
Computer Science, Information Systems
Yao Zhang et al.
Summary: Crowdsourcing is an effective and low-cost method for collecting labels, however, the quality of these labels is often low due to the insufficient professional knowledge of crowd workers. To address this issue, this paper proposes a novel three-stage label integration method called Attribute Augmentation-based Label Integration (AALI).
FRONTIERS OF COMPUTER SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Siavash Ghorbany et al.
Summary: Public-private partnerships (PPPs) are efficient methods for constructing infrastructures, but cost and time overruns have affected their performance. This research proposes a Building Information Modeling (BIM) based model to improve key performance indicators (KPIs) and analyzes the impact of BIM on PPPs using statistical techniques and programming.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ramkumar Harikrishnakumar et al.
Summary: In recent years, bike-sharing systems have been widely adopted in urban cities to provide sustainable transportation options. These systems help reduce overcrowding, pollution, and traffic congestion in highly congested cities. Predicting bike demand is crucial for the successful implementation of bike-sharing systems. To improve demand prediction, this paper proposes the use of quantum computing algorithms for faster computational speed. The construction of Quantum Bayesian Networks (QBN) is illustrated for bike demand prediction, and the quantum and classical solutions are compared using IBM-Qiskit and Netica computing platforms.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
LiMin Wang et al.
Summary: Bayesian network classifiers (BNCs) are powerful tools for encoding dependency relationships among variables in a graph, and this paper proposes a new semi-supervised learning framework that improves classification performance by learning complementary knowledge between training data and testing instances.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yi Ren et al.
Summary: This paper addresses two important issues in learning Bayesian network classifiers: reducing the complexity of network topology and making the learned joint probability distribution fit the data. Ensemble learning algorithms are used to achieve the tradeoff between bias and variance by transforming high-order topology into low-order ones. The proposed algorithm, called random Bayesian forest (RBF), achieves remarkable classification performance compared to state-of-the-art out-of-core BNCs.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Alta de Waal et al.
Summary: To enhance model trustworthiness, recent research focuses on explainable artificial intelligence. In this paper, Bayesian Networks are used to describe travel behavior, incorporating various variables and providing explanations for observed behavior. Two case studies are conducted to assist policymakers in making evidence-based decisions.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Ziqi Chen et al.
Summary: Crowdsourcing is an efficient way to obtain multiple noisy labels for each unlabeled instance. Label integration methods aim to infer the true label of each instance from its multiple noisy labels. This paper proposes a novel label integration method called LAWMA, which improves the performance by augmenting and weighting the labels.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Limin Wang et al.
Summary: This research focuses on improving the performance of Bayesian network classifiers by using a double weighting scheme in AODE. Experimental evaluations show that attribute weighting and model weighting are complementary, and DWAODE demonstrates significant advantages in terms of zero-one loss, bias-variance decomposition, RMSE, Friedman and Nemenyi tests.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Automation & Control Systems
Limin Wang et al.
Summary: This paper introduces a method to measure, describe, and evaluate causal relationships within the framework of Bayesian network learning, and empirically demonstrates its competitive performance in classification and causal interpretation.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Automation & Control Systems
Limin Wang et al.
Summary: This paper introduces a novel algorithm for measuring, describing and evaluating causal relationships in Bayesian network learning framework, and demonstrates competitive classification performance and excellent causal interpretation compared to state-of-the-art Bayesian network classifiers on multiple datasets through a series of experiments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
He Kong et al.
Summary: The paper proposes a novel approach, averaged tree-augmented one-dependence estimators (ATODE), which relaxes the independence assumption of AODE by exploring higher-order conditional dependencies between attributes. Experimental results on 36 datasets demonstrate that the proposed approach can achieve competitive or better classification performance compared to state-of-the-art learners.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Huan Zhang et al.
Summary: Naive Bayes algorithm remains one of the top 10 data mining algorithms, but its conditional independence assumption is rarely true in real-world applications. This study introduces a new improved model called AIWNB, which combines attribute weighting and instance weighting into one uniform framework, to address this issue effectively.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Zhiyi Duan et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Yang Liu et al.
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Marco Benjumeda et al.
ARTIFICIAL INTELLIGENCE
(2019)
Article
Computer Science, Artificial Intelligence
Brandon Malone et al.
Article
Computer Science, Artificial Intelligence
Silvana Badaloni et al.
Article
Computer Science, Artificial Intelligence
Sang-Hyeun Park et al.
Article
Computer Science, Artificial Intelligence
Liangxiao Jiang et al.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
(2012)
Article
Computer Science, Artificial Intelligence
Liangxiao Jiang et al.
KNOWLEDGE-BASED SYSTEMS
(2012)
Article
Computer Science, Artificial Intelligence
Ying Yang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2007)
Article
Computer Science, Artificial Intelligence
Ioannis Tsamardinos et al.