Relevant bibliographies by topics / Naive Bayes fusion

Academic literature on the topic 'Naive Bayes fusion'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Naive Bayes fusion"

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Zhou, Xiaoliang, Donghua Wu, Zitong You, Dongyang Wu, Ning Ye, and Li Zhang. "Adaptive Two-Index Fusion Attribute-Weighted Naive Bayes." Electronics 11, no. 19 (September 29, 2022): 3126. http://dx.doi.org/10.3390/electronics11193126.

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Naive Bayes (NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independence assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these methods, due to its high efficiency and easy implementation, the filter-attribute-weighted NB methods have received great attentions. However, there still exist several challenges, such as the poor representation ability for a single index and the fusion problem of two indexes. To overcome the above challenges, we propose a general framework of an adaptive two-index fusion attribute-weighted NB (ATFNB). Two types of data description category are used to represent the correlation between classes and attributes, the intercorrelation between attributes and attributes, respectively. ATFNB can select any one index from each category. Then, we introduce a regulatory factor β to fuse two indexes, which can adaptively adjust the optimal ratio of any two indexes on various datasets. Furthermore, a range query method is proposed to infer the optimal interval of regulatory factor β. Finally, the weight of each attribute is calculated using the optimal value β and is integrated into an NB classifier to improve the accuracy. The experimental results on 50 benchmark datasets and a Flavia dataset show that ATFNB outperforms the basic NB and state-of-the-art filter-weighted NB models. In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive regulatory factor β. Auxiliary experimental results demonstrate the improved model significantly increases the accuracy compared to the original model without the adaptive regulatory factor β.
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Ou, Guiliang, Yulin He, Philippe Fournier-Viger, and Joshua Zhexue Huang. "A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier." Applied Sciences 12, no. 20 (October 17, 2022): 10443. http://dx.doi.org/10.3390/app122010443.

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The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC).
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Zhang, Yinghui, Hongjun Wang, Hanxiong Liu, Yan Yang, and Qin Chen. "Medical Image Segmentation Fusion Based on Finite Naive Bayes Mixture Model." Journal of Medical Imaging and Health Informatics 6, no. 8 (December 1, 2016): 1865–71. http://dx.doi.org/10.1166/jmihi.2016.1939.

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Chen, Xiaoping, Jiamin Lin, Han Huang, and Yunpeng Huang. "Impact Factors on Posterior Modified Transfacet Debridement for Thoracic Spinal Tuberculosis Basing on Regression and Classification Analysis." Scientific Programming 2020 (August 1, 2020): 1–6. http://dx.doi.org/10.1155/2020/8892815.

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Posterior transfacet approach has been proved to be a safe and effective access to treat thoracic disc herniation. However, the influencing factors of posterior modified transarticular debridement for thoracic tuberculosis have not been reported in the clinical literature. From 2009 to 2014, 37 patients with TST underwent a posterior modified transfacet debridement, interbody fusion following posterior instrumentation, under the cover of 18 months of antituberculosis chemotherapy. The patients were evaluated preoperatively and postoperatively in terms of Frankel Grade, visual analog scale (VAS) pain score, kyphotic Cobb angle, and bone fusion. Blood loss (positive correlation) and focal debridement (positive correlation) could affect operative time. Operative time (positive correlation) could affect blood loss. While, age (positive correlation), PostE (negative correlation), and T_FocalDebridement (positive correlation) could affect bone fusion. The accuracy of naive bayes classifier model is 86.11%. Our preliminary results show that blood loss and focal debridement could affect operative time; operative time could affect blood loss; age, PostE, and T_FocalDebridement could affect bone fusion; the naive Bayes classifier model can predict the KirkaldyWillis accurately.
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Sun, Shuang, Li Liang, Ming Li, and Xin Li. "Multidamage Detection of Bridges Using Rough Set Theory and Naive-Bayes Classifier." Mathematical Problems in Engineering 2018 (May 27, 2018): 1–13. http://dx.doi.org/10.1155/2018/6752456.

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This paper is intended to introduce a two-stage detection method to solve the multidamage problem in bridges. Vibration analysis is conducted to acquire the dynamic fingerprints which are regarded as information sources. Bayesian fusion is used to integrate these sources and preliminarily locate the damage. Then, the RSNB method which combines rough set theory and Naive-Bayes classifier is proposed to simplify the sample dimensions and fuse the remaining attributes for damage extent detection. A numerical simulation of a real structure, the Sishui Bridge in Shenyang, China, is conducted to validate the effectiveness of the proposed detection method. Data fusion based method is compared with single-valued index method at the damage localization stage. The proposed RSNB method is compared with the Back Propagation Neural Network (BPNN) method at the damage qualification stage. The results show that the proposed two-stage damage detection method has better performances in regard to transparency, accuracy, efficiency, noise robustness, and stability. Furthermore, an ambient excitation modal test was carried out on the bridge to obtain the vibration responses and assess the damage condition with the proposed method. This novel approach is applicable for early damage detection and provides a basis for bridge management and maintenance.
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Wang, Yi, Yuhao Huang, Kai Yang, Zhihan Chen, and Cheng Luo. "Generator Fault Classification Method Based on Multi-Source Information Fusion Naive Bayes Classification Algorithm." Energies 15, no. 24 (December 19, 2022): 9635. http://dx.doi.org/10.3390/en15249635.

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The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical Naive Bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%.
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Femina, Bahari T., and Sudheep Elayidom M. "A Novel Fuzzy Linguistic Fusion Approach to Naive Bayes Classifier for Decision Making Applications." International Journal on Advanced Science, Engineering and Information Technology 10, no. 5 (October 19, 2020): 1889. http://dx.doi.org/10.18517/ijaseit.10.5.8186.

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Anisuzzaman, D. M., and Abdus Salam. "Authorship Attribution for Bengali Language Using the Fusion of N-Gram and Naive Bayes Algorithms." International Journal of Information Technology and Computer Science 10, no. 10 (October 8, 2018): 11–21. http://dx.doi.org/10.5815/ijitcs.2018.10.02.

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Yang, Qiufen, and Yan Lu. "Driving Detection Based on the Multifeature Fusion." Journal of Control Science and Engineering 2022 (June 30, 2022): 1–7. http://dx.doi.org/10.1155/2022/8266295.

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In order to solve the problems of facial feature localization and driver fatigue state identification methods in driving fatigue detection, a driving detection method based on the multifeature fusion was proposed. This method uses a supervised descent algorithm to simultaneously locate multiple facial features of drivers. On the basis of blink, yawn and nod judgment, multiple characteristic values of blink frequency, yawn frequency, and nod frequency of drivers were extracted to establish a fatigue detection sample database, and a naive Bayes classifier was constructed to judge fatigue. When the driver appears fatigue driving, warning information is given in time in order to prevent traffic accidents. The experimental results show that two sample videos were selected for testing. The accuracy rate of video sample 1 and video sample 2 was 94.74% and 95.00%, respectively. Conclusion. In the actual driving environment video test results, the discriminant average accuracy of a driver fatigue state reaches 94.87%, which has a good performance.
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Soares, Elaine Anita De Melo Gomes, and Ronei Marcos Moraes. "Fusion of Online Assessment Methods for Gynecological Examination Training: a Feasibility Study." TEMA (São Carlos) 19, no. 3 (December 17, 2018): 423. http://dx.doi.org/10.5540/tema.2018.019.03.423.

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The objective of this paper was to determine if a fusion of online assessment methods is a feasible methodology for online assessment of performance of users inside virtual reality simulators. Three different forms of the Fuzzy Naive Bayes method based on statistical distributions were used to assess specific tasks and the fusion of information was performed by a Weighted Majority Voting system. Data was compiled representing a portion of the Gynecological Exam, which is a checkup examination that is routinely performed for women and is paramount in finding earlier cases of cervical cancer. Confusion matrices and Kappa coefficients were obtained using a Monte Carlo simulation for this method. From the analysis of these results, it is possible to confirm that this method performed well, with a substantial agreement degree.
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Dissertations / Theses on the topic "Naive Bayes fusion"

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Arif-Uz-Zaman, Kazi. "Failure and maintenance information extraction methodology using multiple databases from industry: A new data fusion approach." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/116354/1/Kazi_Arif-Uz-Zaman_Thesis.pdf.

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This study develops a new method to identify a vital input, i.e. failure times of an asset, to reliability models from multiple but commonly-available industrial maintenance databases. A text mining approach is employed to extract useful features from unstructured free texts of different maintenance work records. The proposed method is further developed using Active Learning algorithms to improve the robustness of the results. The outcomes of this study can be used to develop advanced and applicable reliability models from historical maintenance databases, which were not effectively utilised before. Two industry case studies were conducted to justify the method.
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Conference papers on the topic "Naive Bayes fusion"

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Aneja, Saloni, and Sangeeta Lal. "Effective asthma disease prediction using naive Bayes — Neural network fusion technique." In 2014 International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2014. http://dx.doi.org/10.1109/pdgc.2014.7030730.

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Wu, Yunfeng, and S. C. Ng. "Combining Neural Learners with the Naive Bayes Fusion Rule for Breast Tissue Classification." In 2007 2nd IEEE Conference on Industrial Electronics and Applications. IEEE, 2007. http://dx.doi.org/10.1109/iciea.2007.4318498.

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Chen, Fu-Chen, and Mohammad R. Jahanshahi. "Video-based crack detection using deep learning and Nave Bayes data fusion." In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, edited by Hoon Sohn. SPIE, 2018. http://dx.doi.org/10.1117/12.2296772.

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Yang, Zhuo, Jaehyuk Kim, Yan Lu, Ho Yeung, Brandon Lane, Albert Jones, and Yande Ndiaye. "A Multi-Modal Data-Driven Decision Fusion Method for Process Monitoring in Metal Powder Bed Fusion Additive Manufacturing." In 2022 International Additive Manufacturing Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/iam2022-96740.

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Abstract Data fusion techniques aim to improve inference results or decision making by ‘combining’ multiple data sources. Additive manufacturing (AM) in-situ monitoring systems measure various physical phenomena and generate multiple types of data. Data types that occur at different scales and sampling rates during a build process. Data types that can be used to monitor the state of that process. Monitoring typically requires software tools to analyze multiple data sources. There are two reasons. First, data only from an individual data source may not be accurate enough or large enough to monitor the process stat. Second, a single source will be limited by the relevancy of the observations, signal-to-noise ratio, or other measurement uncertainties. This work proposes a decision-level, multimodal, data fusion method that combines multiple, in-situ, AM monitoring data sources to improve overall, process-monitoring performance. The work is based on a recent, laser powder bed fusion (LPBF) experiment that was conducted to create overhang surfaces throughout a 3D part. The data from that experiment is used to illustrate and validate the proposed method. The overhang features were designed with different shapes. angles, and build locations. The features are formed using constant laser power and scan speed. A high-frequency, coaxial, melt-pool, imaging system and a low-frequency layerwise staring camera are the two, in-situ, monitoring, data sources used in that experiment. The Naïve Bayes and the k-nearest-neighbors algorithms are first applied to each data set for overhang feature detection. Then both hard voting and soft voting are adopted in fusing the classification outcomes. The results show that while none of the individual classifiers are perfect in detecting overhang features, the fused decision of the 324 test samples achieved 100% detection accuracy.
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Losi, Enzo, Mauro Venturini, Lucrezia Manservigi, and Giovanni Bechini. "Ensemble Learning Approach to the Prediction of Gas Turbine Trip." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-80372.

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Abstract In the field of gas turbine (GT) monitoring and diagnostics, GT trip is of great concern for manufactures and users. In fact, due to the number of issues that may cause a trip, its occurrence is not infrequent, and its prediction is a quite unexplored field of research. This is demonstrated by the fact that, despite its relevance, a comprehensive study on the reliability of predicting GT trip has not been proposed yet. To fill this gap, this paper investigates the fusion of five data-driven base models by means of voting and stacking, in order to increase base model accuracy and improve prediction robustness. The five benchmark supervised Machine Learning and Deep Learning classifiers are k-Nearest Neighbors, Support Vector Machine, Naïve Bayes, Decision Trees, and Long Short-Term Memory neural networks. While voting just averages the predictions of base models, without providing additional pieces of information, stacking is a technique used to aggregate heterogeneous models by training an additional machine learning model (namely, stacked ensemble model) on the predictions of the base models. The analyses carried out in this paper employ filed observations of both safe operation and trip events, derived from a large fleet of industrial Siemens GTs in operation. The results demonstrate that the stacked model provides higher accuracy than base models and also outperforms voting by proving more effective, especially when the reliability of the prediction of base models is poor.
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