Efficient weighted naive bayes classifiers to predict air quality index

Sethi, Jasleen Kaur; Mittal, Mamta

doi:10.1007/s12145-021-00755-7

Cited by 15 publications

(4 citation statements)

References 25 publications

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“…Confusion Matrix adalah alat lain untuk memvisualisasikan hasil pembelajaran sistem; itu menunjukkan setidaknya dua kategori sekaligus [19]. Ilustrasi hasil ramalan Confusion Matrix untuk dua kelas dapat dilihat pada tabel di bawah ini.…”

Section: K Evaluasi Confusion Matrixunclassified

Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Nurjannah,

Cipta,

Aprilia

2023

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The Indonesian government sometimes faces difficulties in dealing with poverty problems. The Indonesian government utilizes a number of programs and stimulants to overcome the problem of poverty. The government's PKH program offers conditional assistance to low-income families who have been designated as PKH recipient households. PKH provision is still below optimal standards, this may be because the data used is not updated frequently. To assist village officials in determining which residents are eligible to receive PKH assistance, this research tries to classify the eligibility of recipient residents in Cinta Rakyat Village. With the Weighted Naive Bayes method, classification calculations are not only based on probability distributions but also by adding weights to each attribute to the class. Assisted with Laplace Smoothing to avoid a probability value of 0. As a result, there are eight factors that determine a person's eligibility to receive PKH assistance, including age, occupation, income, number of family members, number of dependent school children, quality of house, type of floor, and type of walls. As well as classification into eligible and non-eligible groups. And obtained test results using the Confusion Matrix with an accuracy value of 95.65%, error rate of 4.34%, sensitivity of 100% and specificity of 94.74%. To identify village communities who deserve PKH assistance, Cinta Rakyat Village administrators can use the findings of this research.

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Section: K Evaluasi Confusion Matrixunclassified

Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Nurjannah,

Cipta,

Aprilia

2023

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“…The Naïve Bayes classifier is surprisingly effective in practice since its classification decision may often be correct even if its probability estimates are inaccurate [33]. Recently in the weather classification area, weighted Naïve Bayes classifiers performed better than the traditional Naïve Bayes, Support Vector Machines, and Neural Network classifiers with respect to various performance metrics-accuracy, average precision, average recall, error rate, and F1 score [34]. It is a method of using covariance as a weight to supplement the assumption of independence between characteristics.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Development of Bus Routes Reorganization Support Software Using the Naïve Bayes Classification Method

Suh¹,

Jeong²

2022

Sustainability

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Reorganizing city bus routes is generally accomplished by designing bus supply methods to meet passenger demand. The bus supply method involves establishing bus routes and planning their schedules. The actual bus route reorganization decisions are not determined simply by balancing passenger demand and bus supply, but are based on other complex interests, such as bus routes that must exist for welfare but where profits are low. Machine learned prediction models could be helpful when considering such factors in the decision-making process. Here, the Naïve Bayes algorithm was applied to develop the classifier model because of its applicability, even with a limited amount of training data. As the input characteristics for the Naïve Bayes algorithm, data for each individual bus route were featured and cleansed with the actual route improvement decisions. A number of classification models were created by changing training sets and then compared in terms of classification performance such as accuracy, precision, and recall. Modeling and tests were conducted to show how Naïve Bayes classifiers learned in the form of supervised learning can help the route reorganization work. Results from a local governments’ actual route reorganization study were used to train and test the proposed machine learning classification model. As the main contribution of this study, a prediction model was developed to support shortening decision-making for each route, using machine learning algorithms and actual route reorganization research case data. Results verified that such an automatic classifier, or initial route decision proposal software, can provide intuitive support in actual route reorganization research.

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“…Typically, classification serves as a valuable tool for making decisions in situations involving complex problems and extensive datasets. Examples of classification techniques includeaïve Bayes [1,2], decision tree-based approaches [3,4], rule-based methods [5,6], upport vector machines (SVMs) [7,8], neural networks [9,10], k-nearest neighbor (KNN) [11,12], and statistical methods like logistic regression [13,14]. Support vector machine (SVM) and logistic regression (LR) represent two widely utilized supervised classification methods [15,16].…”

Section: Introductionmentioning

confidence: 99%

Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model

Nordin,

Mustafa,

Lola

et al. 2023

Bioengineering

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Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.

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Efficient weighted naive bayes classifiers to predict air quality index

Cited by 15 publications

References 25 publications

Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Development of Bus Routes Reorganization Support Software Using the Naïve Bayes Classification Method

Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model

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