Bayesian Classification Algorithm Research Articles

Tracking full posterior in online Bayesian classification learning: a particle filter approach

The rapid growth of data volume and velocity is challenging traditional methods of classification, making it impossible to store so much data in memory. Developing online classification methods is becoming increasingly important. Online classification approaches have been subject to several assumptions in recent years, such as an independent and consistent distribution of initial values. In this paper, we present a general online Bayesian classification algorithm that can be adapted to handle streaming data based on the Monte Carlo fusion method. Rather than storing the entire raw data, we only update the posterior with historical data and the current batch of data. In addition, the proposed method has the capability of being applied to a wide range of situations with a high degree of accuracy, even in the case of data imbalances and discrepancies between sub-posterior distributions. The proposed algorithm was validated and evaluated through a comprehensive simulation study.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

An Inquiry into the Translation of English Literary Metaphors Based on Text Mining Techniques

Abstract In this paper, the vector space model is used to convert the English literature text to be processed into a feature vector composed of feature items, and after adding weights to the feature items, it is composed into an N -dimensional space. After calculating the text similarity using the vector angle cosine, the knowledge base of English literature metaphors and an automatic recognition model are constructed by combining them with a Bayesian classification algorithm. Starting from the research purpose, we determine the research object and tools and analyze an example of English literary metaphor translation through the combination of simulation analysis and statistical analysis. The results show that the running speed of NBC is 10.2% faster than that of CFC, the speed of SOFMC is 70.1% faster than that of CFC, the accuracy rate of NBC is 32.8% higher than that of SOFMC, and that of CFC is 41.8% higher than that of SOFMC, and in terms of the combined running speed and accuracy rate, NBC performs better than SOFMC and CFC, i.e., it shows that the Bayesian classification algorithm is useful for dealing with English metaphor recognition and Chinese translation with excellent performance. In terms of lexical translation accuracy, the translated English literary metaphor texts with labeled lexical categories are significantly higher than the translated English literary metaphor texts without labeled lexical categories, with a difference value of 6.31%. This study extends the scope of cognitive linguistics research and offers a guiding reference value for translating English literary metaphors.

Design of cultural and creative experience products in scenic spots based on CORE model

Abstract Under the background of “cultural and tourism integration”, scenic cultural and creative experience products are an important means to enhance the communication power of scenic spots and generate income. In this paper, the CORE model is used as the design concept, and the CORE design process for scenic cultural and creative products is constructed. The plain Bayesian classification algorithm is used to extract review feedback from the product, and the vectorization of the extracted reviews is achieved using the FastText model. The vectorized reviews are input into the logistic regression classification model to obtain sentiment classification results. A fused sentiment feature vector is constructed by combining Apirior and a sentiment dictionary aimed at separating sentiment vector classification. Finally, the experience effect of cultural and creative products is explored as an example of X scenic spot, and the optimization strategy of product design is explored. The attribute feature of X Scenic Spot’s cultural and creative experience product, “color” embraces four emotional attributes, and the corresponding adaptations are 4.77, 5.42, 5.40, and 5.38, respectively. The SDs of the emotional attributes of the existing product are The SDs of the emotional attributes of the existing products are 1.141, 1.025, and 0.184, respectively, and the differences between the product descriptions and the users’ opinions on the perceptual attributes are large, which provides the evaluation correlation in the CORE model. Under the guidance of the CORE model, four aspects of cultural and creative marketing, scenic area IP, consumer needs and product experience dimensions are proposed.

Analysis of the evolutionary path of early civilization agriculture based on the plain Bayesian model

Abstract First, this paper focuses on the problem of nonlinear association between attributes and categories, combines inverse document frequency information and deep attribute weighting, improves the distance correlation coefficient, and proposes an attribute-weighted polynomial plain Bayesian text classification algorithm based on the improved distance correlation coefficient. Then a specific scheme for stage classification of early Chinese civilization provides a theoretical premise and expository framework for systematically exploring the relationship between primitive agricultural types and early Chinese civilization and also provides conditions for forming the agricultural revolution of early Chinese civilization in the Central Plains region. Finally, according to the research objectives, the 7000-year-old Ningzhen area was selected as the research object, and the attribute-weighted polynomial plain Bayesian text classification algorithm was used to analyze the data of the research object. The results show that: in the Neolithic burials of the North Yingying culture period alone, there are 240 burial relics with a total of 1,580 pieces of all burial relics, including 554 pieces of stone production workers, but the fundamental reason is the active change of human for the survival crisis. This study is a preliminary attempt to conduct an interdisciplinary study on the history of agriculture and civilization and provides an academic path for the sublimation of agricultural history to the study of the history of agricultural civilization.

Study on the current situation and countermeasures of the history of health culture development in Shaanxi based on the background of big data

Abstract First, this paper studies the derivation process of the plain Bayesian classification algorithm in big data technology, which is built based on statistics. Then to make up for the feature independence assumption of plain Bayes, the plain Bayesian classification algorithm based on feature weighting is proposed to be optimized mainly by the Laplace algorithm, which improves the classification accuracy to a certain extent. Finally, based on the connotation and characteristics of health culture in Shaanxi, the formation and development of health culture theory in Shaanxi are clarified, and policy suggestions are proposed for the development problems of health culture in Shaanxi and the development level of cultural industry in Shaanxi province. The analysis of the health culture industry in Shaanxi is based on the vegetative Bayesian classification algorithm. The results show that the cultural service industry in Shaanxi Province achieved an output value of 41.309 billion yuan, accounting for 63.9% of the added value of the cultural industry in the province. The cultural manufacturing industry in Shaanxi accounts for a large gap with the national level, with a difference of 19.3 percentage points. This study combines big data technology and the advantages of Shaanxi’s healthy cultural resources to provide reasonable countermeasure suggestions to promote the transformation of Shaanxi from a large cultural province to a strong cultural province.

An Experiment on University English Translation Teaching Model Based on Big Data-Driven Learning

Abstract Exploring the teaching mode of English translation in college to stimulate students’ learning initiative better and improve their English translation ability. In this paper, the basic principles of the plain Bayesian classification algorithm are introduced under big data technology-driven learning, and the conditional independence of plain Bayesian is illustrated. The algorithm and process of probabilistic classification of samples to be judged using great likelihood estimation are introduced, and the advantages and disadvantages of the algorithm are illustrated. Based on the disadvantages of this algorithm, the INB-MI classification algorithm is induced by adjusting the conditional probability after the number of smoothing using mutual information, and the validity is verified for this algorithm. For the university, English translation teaching was analyzed, a task-based English translation teaching model based on a blended environment was proposed, and the model was quantitatively analyzed with the INB-MI classification algorithm for the relevant data. In terms of cognitive types, the overall learning rate of this teaching model was 43.11%, 41.21%, 40.46%, and 38.3% for factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge types, respectively. Regarding instructional evaluation, the percentage of those who agreed was 84.26%. Thus, the task-based English translation teaching model under big data-driven learning can help students integrate their existing knowledge and enhance their learning initiatives.

Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm.

Microarray information is crucial for the identification and categorisation of malignant tissues. The very limited sample size in the microarray has always been a challenge for classification design in cancer research. As a result, by pre-processing gene selection approaches and genes lacking their information, the microarray data are deleted prior to categorisation. In essence, an appropriate gene selection technique can significantly increase the accuracy of illness (cancer) classification. For the classification of high-dimensional microarray data, a novel approach based on the hybrid model of multi-objective particle swarm optimisation (MOPSO) is proposed in this research. First, a binary vector representing each particle's position is presented at random. A gene is represented by each bit. Bit 0 denotes the absence of selection of the characteristic (gene) corresponding to it, while bit 1 denotes the selection of the gene. Therefore, the position of each particle represents a set of genes, and the linear Bayesian discriminant analysis classification algorithm calculates each particle's degree of fitness to assess the quality of the gene set that particle has chosen. The suggested methodology is applied to four different cancer database sets, and the results are contrasted with those of other approaches currently in use. The proposed algorithm has been applied on four sets of cancer database and its results have been compared with other existing methods. The results of the implementation show that the improvement of classification accuracy in the proposed algorithm compared to other methods for four sets of databases is 25.84% on average. So that it has improved by 18.63% in the blood cancer database, 24.25% in the lung cancer database, 27.73% in the breast cancer database, and 32.80% in the prostate cancer database. Therefore, the proposed algorithm is able to identify a small set of genes containing information in a way choose to increase the classification accuracy. Our proposed solution is used for data classification, which also improves classification accuracy. This is possible because the MOPSO model removes redundancy and reduces the number of redundant and redundant genes by considering how genes are correlated with each other.

Cost-sensitive classification algorithm combining the Bayesian algorithm and quantum decision tree

This study highlights the drawbacks of current quantum classifiers that limit their efficiency and data processing capabilities in big data environments. The paper proposes a global decision tree paradigm to address these issues, focusing on designing a complete quantum decision tree classification algorithm that is accurate and efficient while also considering classification costs. The proposed method integrates the Bayesian algorithm and the quantum decision tree classification algorithm to handle incremental data. The proposed approach generates a suitable decision tree dynamically based on data objects and cost constraints. To handle incremental data, the Bayesian algorithm and quantum decision tree classification algorithm are integrated, and kernel functions obtained from quantum kernel estimation are added to a linear quantum support vector machine to construct a decision tree classifier using decision directed acyclic networks of quantum support vector machine nodes (QKE). The experimental findings demonstrate the effectiveness and adaptability of the suggested quantum classification technique. In terms of classification accuracy, speed, and practical application impact, the proposed classification approach outperforms the competition, with an accuracy difference from conventional classification algorithms being less than 1%. With improved accuracy and reduced expense as the incremental data increases, the efficiency of the suggested algorithm for incremental data classification is comparable to previous quantum classification algorithms. The proposed global decision tree paradigm addresses the critical issues that need to be resolved by quantum classification methods, such as the inability to process incremental data and the failure to take the cost of categorization into account. By integrating the Bayesian algorithm and the quantum decision tree classification algorithm and using QKE, the proposed method achieves high accuracy and efficiency while maintaining high performance when processing incremental sequences and considering classification costs. Overall, the theoretical and experimental findings demonstrate the effectiveness of the suggested quantum classification technique, which offers a promising solution for handling big data classification tasks that require high accuracy and efficiency.

Computer network security and information security technology optimization by using Bayesian classification algorithm

Computer network security and information security technology optimization by using Bayesian classification algorithm

Combined application of ideological and political education and big data Internet technology in the context of education reform

Abstract In the course of today’s socio-political and economic development, along with the advent of the era of big data and the internet, various ideas have emerged and more ideological content and political ideas have continued to emerge. In order to cope with this situation, education reform is constantly innovating, among which the ideological education is particularly important. Various ideological education applications have appeared one after another in the Internet, which allows ideological education to see a new direction of development. The article develops a rigorous computational analysis of the combination of Civic Education and Big Data Internet, applied to the development of a new system. Using decision tree classification algorithm, ID3 algorithm, Bayesian classification algorithm, cohesive hierarchical clustering method for a large number of experimental data processing, the system was completed to build a framework for the combination of big data and Civic Education application. Through data analysis and comparison, it is found that the application of these big data algorithms in the field of Civic Education not only provides more detailed information on Civic Education reform, but also provides objective and comprehensive feedback on the dissemination and learning of Civic Education. In the relevant feedback information, the use of Internet applications for Civic Education reached 100%, Excel was 84%, and all the learning levels of the fixed-point test could reach over 60 points. The above data shows that people have also been able to improve their learning level in Civic Education to a great extent with the Internet technology.

Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction

It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an "excellent classification" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Research on quantitative evaluation of rebar stress based on weak magnetic effect

In order to explore an evaluation method for nondestructive testing of the rebar stress, the weak magnetic field test of the rebar stress was carried out in this paper. The evolution characteristics of the magnetic induction component By along the length direction of the rebar under tensile load were analyzed. The value of the root mean square gradient of the axial magnetic induction intensity KRMSe was introduced. The law of linear decline of characteristic parameters with the stress in the elastic stage was revealed. The relationship between the feature parameters and the lift-off value of the sensor was further analyzed, and the lifting effect of the feature parameters was modified. Based on the correction of the lifting effect, the influence of the initial magnetization on the weak magnetic detection of stress was removed through the simulation test. The naive Bayesian classification algorithm is introduced into the stress classification evaluation. The statistical analysis of the relationship between the modified characteristic parameter KRMSecN and stress is carried out. A stress classification evaluation model is established based on the maximum of posteriori probability. The model can classify the rebar stress in specific ranges with high accuracy. This study provides a new evaluation method for nondestructive testing of the rebar stress state.

Who publishes imaging articles in non-imaging journals? A large sample data-mining study.

To assess the weight of imaging and imaging specialists (i.e., affiliated to a radiology/nuclear medicine department) in publications in non-imaging journals. All articles indexed in English on the Embase database between 1989 and 2019 were extracted. The number and affiliation of authors were determined. A naive Bayesian classifier algorithm was trained to classify abstracts as "imaging" or "non-imaging." The main outcome was the number and position of imaging specialists in the authorship of imaging articles published in non-imaging journals. Analyses per medical specialties and per journal impact factor (IF) were performed. A total of 15,787,825 articles were included with 968,259 (6%) "imaging" articles. The proportion of imaging articles increased over time (+ 370%), quicker than the overall academic output. The proportion of imaging specialist among authors grew from 0.58% in 1989-1994 to 1.54% in 2015-2019. About 20% of imaging articles had ≥ 1 imaging specialist among authors. The proportion of imaging articles decreased with the IF (7.3% for IF 0-2.5 vs. 5.1% for IF > 10, p < 0.001), but the proportion of imaging specialist authors in imaging papers with ≥ 1 imaging specialist author increased with the IF (40% for IF 0-2.5, 53% for IF > 10, p < 0.001). There was significant variability across medical specialties. The weight of imaging articles and imaging specialist among authors in non-imaging journals has increased over time but remains limited. Most of the authors of imaging publications are not imaging specialists. Imaging specialists among authors in imaging papers are associated with a greater IF. • The proportion of imaging specialist authors in non-imaging journals, though small, has increased significantly. • Marked differences are observed according to medical specialties and the reputation/impact factor of the journal. • Collaboration between imaging specialists and non-specialists is associated with publication in higher impact journals.

Implementasi Algoritma Naïve Bayes Classifier Dalam Penerimaan Karyawan

The final research report of this study aims to find out how the employee recruitment procedure at PT. Kreasibeton Nusapada, because human resources are a component that has a very important role in a company. In today's technological age, some companies have used certain methods in making choices to recruit new employees. This is because the assessment used to obtain competent new employees should be an objective assessment. In this research, an assessment method will be developed to make it easier for the HRD (Human Resource Department) to select competent candidates to fill certain positions needed by the company. The assessment will be calculated by assessing all the criteria that must be met by prospective company employees, where the assessment is made with standards predetermined by management. The research produced a suitable candidate to fill the position of HSE (Health Safety Environment) staff at PT Kreasibeton Nusapersada, namely Rizki Vadlin with a final score of 1.75 which was calculated based on the Naïve Bayesian Classifier Algorithm. The results of this study indicate that the process of assessing the competence of prospective employees to fill available positions in the company is proven to produce an objective and effective assessment in accordance with the criteria set by the company's managemen.

Early prediction of acute respiratory distress syndrome complicated by acute pancreatitis based on four machine learning models

BackgroundAcute Respiratory Distress syndrome (ARDS) is a common complication of Acute Pancreatitis (AP) and is associated with high mortality. This study used Machine Learning (ML) to predict ARDS in patients with AP at admission. MethodsThe authors retrospectively analyzed the data from patients with AP from January 2017 to August 2022. Clinical and laboratory parameters with significant differences between patients with and without ARDS were screened by univariate analysis. Then, Support Vector Machine (SVM), Ensembles of Decision Trees (EDTs), Bayesian Classifier (BC), and nomogram models were constructed and optimized after feature screening based on these parameters. Five-fold cross-validation was used to train each model. A test set was used to evaluate the predictive performance of the four models. ResultsA total of 83 (18.04%) of 460 patients with AP developed ARDS. Thirty-one features with significant differences between the groups with and without ARDS in the training set were used for modeling. The Partial Pressure of Oxygen (PaO2), C-reactive protein, procalcitonin, lactic acid, Ca2+, the neutrophil:lymphocyte ratio, white blood cell count, and amylase were identified as the optimal subset of features. The BC algorithm had the best predictive performance with the highest AUC value (0.891) than SVM (0.870), EDTs (0.813), and the nomogram (0.874) in the test set. The EDT algorithm achieved the highest accuracy (0.891), precision (0.800), and F1 score (0.615), but the lowest FDR (0.200) and the second-highest NPV (0.902). ConclusionsA predictive model of ARDS complicated by AP was successfully developed based on ML. Predictive performance was evaluated by a test set, for which BC showed superior predictive performance and EDTs could be a more promising prediction tool for larger samples.

Secret information security system in computer network based on Bayesian classification and nonlinear algorithm

Abstract To solve the problem that most computer network security assessment systems cannot comprehensively analyze data, this article proposes a research method for computer network confidentiality information security system based on the Bayesian classification algorithm. This research takes the computer network security secret-related information security as the core, cleverly uses the EBCA back-propagation model, and comprehensively uses nonlinear functions to propose a computer network secret-related information security application system considering EBCA, which effectively solves the complex cross-linking relationship. The effectiveness of the method is verified by simulation experiments. Simulation experiments show that when the network performance is in the optimal state, the slope is 1, the intercept is 0, and the fitting degree is 1. The method proposed in this article is especially suitable for the nonlinear characteristics of computer network security and can accurately, comprehensively, and systematically reflect the security operation status of network security and confidential information. Then, through the simulation test, it is found that the method in this study can effectively meet the error requirements of equipment integrity and equipment normal rate.

On the Origin of the Dichotomy of Stellar Activity Cycles

The presence of possible correlations between stellar rotation rate Ω and the frequency of the activity cycle ω cyc is still much debated. We implement a new Bayesian classification algorithm based on a simultaneous regression analysis of multiple scaling laws and we demonstrate the existence of two different scalings in the – plane for an extended Mt. Wilson sample of 67 stars. Thanks to metallicity measurements obtained from both ESA Gaia and high-resolution spectroscopy, we argue that the origin of this dichotomy is likely related to the chemical composition: stars whose magnetic cycle frequency increases with rotation rate are less metallic than stars whose magnetic cycle frequency decreases with stellar rotation rates. On the contrary, no clear difference in chromospheric magnetic activity indicators characterizes the two branches.

Automatic classification of document resources based on Naive Bayesian classification algorithm

World Wide Web has become big as the amount of documents collection is increasing rapidly. The automatic classification of document resources based on Naive Bayesian classification algorithm is detailed in this paper. Firstly, this paper introduces the relevant theories of naive Bayes classification and the automatic document classification system. Then, a massive network academic document automatic classification system is designed and implemented. The system uses modular design, including academic document automatic capture module, academic document word document matrix processing module, ontology integration module and semantic driven classification module. Finally, based on the Naive Bayesian classification algorithm, the training set of 12 categories preset is utilized in the professional classification directory of the Ministry of education.. Experiments show that the naive Bayesian classification algorithm can effectively complete the automatic capture, processing and classification of massive academic documents, which can not only improve the classification accuracy, but also reduce the running time of automatic classification. It solves the problems of the integration of two heterogeneous ontology libraries and also the problem that the traditional word vector space cannot meet people's needs for semantic classification.