Naive Bayesian Classification Method Research Articles

Study on environmental monitoring classification system based on improved bayesian algorithm

Abstract The commonly used methods for classifying water quality data are based on water quality parameters. The naive Bayesian classification method can be applied to substitute different evaluation criteria and selected water quality parameters for different water bodies. Compared to other water quality data classification methods, naive Bayesian classification methods have advantages such as simple calculation, high classification accuracy, and strong universality. However, this method overlooks the correlation between various water quality parameters and categories. To address the issues of poor universality, computational complexity, and low accuracy of traditional water quality data classification methods, a water quality data classification method based on weighted naive Bayes is proposed. This method comprehensively considers the impact of water quality attributes and their values on the classification results and replaces the original naive Bayes with weighted attribute conditional probabilities to make the classification results as close as possible to the actual category of the sample. The results indicate that this method exceeds 94% accuracy and can be directly utilized as a water quality classification module in water quality monitoring systems.

Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

In order to meet the differentiated needs of students and improve the satisfaction of college mental health textbook recommendation, a college mental health textbook recommendation scheme based on improved deep learning algorithm is proposed. Based on the analysis of the principle of deep learning data recommendation system, the data interest is calculated according to the browsing records of students on college mental health textbooks. Combine the deep learning algorithm and collaborative filtering algorithm to collect the demand data of college mental health textbooks, then use the Naive Bayesian classification method to divide the college mental health textbooks into interested and uninterested parts, and recommend the interested college mental health textbooks to the students in need. Experiments show that the longest recommendation time of the college mental health textbook recommendation scheme based on the improved deep learning algorithm proposed in this paper is 4.5 min, the highest recommended recall rate is 95.18%, the average accuracy is 97.2%, the highest content richness is 0.8, the system stability coefficient is 1.06, and the overall average praise rate is 97.79%. It has a good recommendation effect.

Determination of the Children Classification with Special Needs in Extraordinary High School in Banjarmasin Using the Naive Bayes

Children with special needs with different physical characteristics are very easy to distinguish. However, in children with special needs with different psychological characteristics, there are difficulties in determining the classification of the child. Children with mental disorders are children who have deviations in the ability to think critically, logically in responding to the world around them. This study determined the classification of children with special needs, the decision support system can analyze using the naive bayes classification (NBC) method. The result showed that Naïve Bayes Classifier algorithm can be used as the basis for the decision-making process by the Special High School Special Education Foundation (SMALB YPLB) Banjarmasin. In this study, it is hoped that using an application based on the Naive Bayesian Classification method can be used as an alternative calculation that is more effective than calculations using the manual method, with the achievement of conformity of the results reaching 80%.

Exponential Loss Minimization for Learning Weighted Naive Bayes Classifiers

The naive Bayesian classification method has received significant attention in the field of supervised learning. This method has an unrealistic assumption in that it views all attributes as equally important. Attribute weighting is one of the methods used to alleviate this assumption and consequently improve the performance of the naive Bayes classification. This study, with a focus on nonlinear optimization problems, proposes four attribute weighting methods by minimizing four different loss functions. The proposed loss functions belong to a family of exponential functions that makes the optimization problems more straightforward to solve, provides analytical properties of the trained classifier, and allows for the simple modification of the loss function such that the naive Bayes classifier becomes robust to noisy instances. This research begins with a typical exponential loss which is sensitive to noise and provides a series of its modifications to make naive Bayes classifiers more robust to noisy instances. Based on numerical experiments conducted using 28 datasets from the UCI machine learning repository, we confirmed that the proposed scheme successfully determines optimal attribute weights and improves the classification performance.

Differential diagnosis of fetal large ventricular septal defect and tetralogy of Fallot based on big data analysis.

To analyze echocardiographic parameters of fetal large ventricular septal defect (VSD) and tetralogy of Fallot (TOF) in the context of multicenter data and big data analysis because these two diseases are often misdiagnosed in fetuses, and to find the key parameters for the differential diagnosis of these two diseases. A total of 305 cases of large VSD and 192 cases of TOF diagnosed by fetal echocardiography from August 2010 to July 2016 from the database of Beijing Key Laboratory of Fetal Heart Defects were analyzed. Quantile regression of the 48 echocardiographic parameters of the 6272 normal fetuses from seven Chinese medical institutions was performed to determine the Q-score. The forward selection method and the naive Bayesian classification method were used to analyze the core differential diagnostic variables of fetal TOF and VSD. The Q-score of the internal diameter of the aorta (AO Q-score), the ratio of the diameter of the pulmonary artery to the internal diameter of the aorta (PA/AO), and the Q-score of the ratio of the diameter of the pulmonary artery to the internal diameter of the aorta (PA/AO Q-score) were key parameters for the differential diagnosis of fetal large VSD and TOF. PA/AO was the primary parameter, with an area under the receiver operating characteristic curve of 0.951. These findings provide a new method for the prenatal diagnosis of large VSD and TOF and a theoretical basis for the intelligent diagnosis of large VSD and TOF.

Iteration-based naive Bayes sentiment classification of microblog multimedia posts considering emoticon attributes

Microblog (such as Weibo) is an integrated social platform of vital importance in the internet age. Because of its diversity, subjectivity and timeliness, microblog is popular among public. In order to perform sentiment classification on microblog posts and overcome the limitation of text information, a fine-grained sentiment analysis method is proposed, in which emoticon attributes are considered. Firstly, the microblog texts are pre-processed to remove some stop words and noise information such as links. Then the data is matched in the sentiment lexicon, and when the first matching succeeds, the second matching is performed in the emoticon dictionary. The emoticons in the emoticon dictionary are transformed into vector form. Through these matching, the emotional features are vectorized and other text features are considered. Finally, the iterative-based naive Bayesian classification method is used for sentiment classification. The experiment results show that emoticons have obvious effect on facilitating the sentiment classification of microblog posts, and the proposed sentiment classification method achieved better than average results in term of classification accuracy compared with state-of-art techniques.

Emotional Analysis of Public Opinions in Colleges and Universities:Based on Naive Bayesian Classification Method

In this paper, we use the emotion dictionary to process and express the public opinion texts of colleges and universities. We construct the public opinion texts of colleges and universities sentiment emotional classifier based on Naive Bayesian theory, and then judge the emotional tendency of public opinion texts of colleges and universities. Experiments show that the method has the characteristics of fast classification and high accuracy, and is suitable for the public opinion system of colleges and universities. The method facilitates the university management’s interpretation and mastery of the public opinions related to the school in the network, promotes and guides the positive energy in the communication network, corrects the vulgar and biased content, prevents the forwarding and dissemination of inappropriate speech, and establishes a positive campus environment among the teachers and students of the whole school.

Landslide hazards mapping using uncertain Naïve Bayesian classification method

Landslide hazard mapping is a fundamental tool for disaster management activities in Loess terrains. Aiming at major issues with these landslide hazard assessment methods based on Naive Bayesian classification technique, which is difficult in quantifying those uncertain triggering factors, the main purpose of this work is to evaluate the predictive power of landslide spatial models based on uncertain Naive Bayesian classification method in Baota district of Yan’an city in Shaanxi province, China. Firstly, thematic maps representing various factors that are related to landslide activity were generated. Secondly, by using field data and GIS techniques, a landslide hazard map was performed. To improve the accuracy of the resulting landslide hazard map, the strategies were designed, which quantified the uncertain triggering factor to design landslide spatial models based on uncertain Naive Bayesian classification method named NBU algorithm. The accuracies of the area under relative operating characteristics curves (AUC) in NBU and Naive Bayesian algorithm are 87.29% and 82.47% respectively. Thus, NBU algorithm can be used efficiently for landslide hazard analysis and might be widely used for the prediction of various spatial events based on uncertain classification technique.

Incremental Naïve Bayesian Learning Algorithm based on Classification Contribution Degree

In order to improve the ability of gradual learning on the training set gotten in batches of Naive Bayesian classifier, an incremental Naive Bayesian learning algorithm is improved with the research on the existing incremental Naive Bayesian learning algorithms. Aiming at the problems with the existing incremental amending sample selection strategy, the paper introduced the concept of sample Classification Contribution Degree in the process of incremental learning, based on the comprehensive consideration about classification discrimination, noisy and redundancy of the new training data. The definition and theoretical analysis of sample Classification Contribution Degree is given in this paper. Then the paper proposed the incremental Naive Bayesian classification method based on the Classification Contribution Degree. The experimental results show that the algorithm simplified the incremental learning process, improved the classification accuracy of incremental learning.