Comparative Analysis of Bayes Net Classifier, Naive Bayes Classifier and Combination of both Classifiers using WEKA

Nakra, Abhilasha; Duhan, Manoj

doi:10.5815/ijitcs.2019.03.04

Cited by 8 publications

(6 citation statements)

References 15 publications

(15 reference statements)

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“…Tools yang sering digunakan dalam mengolah data mining antara lain: Rapit Miner, WEKA, KNIME, Orange dan Scikit learn. Setiap tools memiliki kelebihan dan kekurangan masing-masing, namun dari tools-tools yang ada, Rapit Miner, WEKA dan KNIME yang paling populer digunakan dalam pengolahan data mining [21]…”

Section: Data Miningunclassified

Pengembangan Layanan Kependudukan Dan Pencatatan Sipil Menggunakan Algoritma Naïve Bayes

Yudhana¹,

Riadi²,

Djou³

2022

Jur. Ris. Kom.

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The “pick-up ball service” program or commonly called mobile services is a form of developing services for the Population and Civil Registration Service (disdukcapil), especially Alor NTT Regency, which aims to reach people who have difficulty getting services due to various obstacles, including due to long distances and difficult access to service centers. Disdukcapil is an implementing agency in the district that is obliged to ensure that all residents, both Indonesians and foreigners, are recorded in the population database, have a NIK, and population documents. In its implementation, not all villages can be served due to insufficient time and funds, so it is necessary to select villages that are worthy of being served by “ball pick up service” or mobile service programs. So far, the determination of villages that are worthy of the program is still determined manually so that it is inseparable from the element of subjectivity. In this study, a classification method was used in data mining using the naïve bayes algorithm. Naïve bayes is one of the algorithms that is able to predict well by calculating the probability of each class and comparing them, Naïve bayes is used to calculate probability values from training data so that it can predict which villages are feasible for the program. The study used 25 training data taken from previous dukcapil service data and data on the distance and difficulty of access to the village, which were tested and simulated using the naïve bayes algorithm of weka software. The purpose of this study was to obtain accurate information about the villages in Alor district that deserve the program. The results of the process using WEKA software found that from 25 tuples used as test data, it resulted in 100% accuracy .

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Section: Data Miningunclassified

Pengembangan Layanan Kependudukan Dan Pencatatan Sipil Menggunakan Algoritma Naïve Bayes

Yudhana¹,

Riadi²,

Djou³

2022

Jur. Ris. Kom.

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“…In this technique, attributes are nominal, and no missing value parameter is used as they are replaced globally. The output of this classification algorithm can be represented by a graph [34]. The graphical representation of BayesNet classification for the proposed system is shown in Fig.…”

Section: Bayesnet Classificationmentioning

confidence: 99%

Predicting COVID-19 Based on Environmental Factors With Machine Learning

Abdulkareem¹,

Sani²,

Sahran³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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The coronavirus disease 2019 has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet) based on the confirmed cases and weather variables such as temperature, humidity, wind and precipitation are developed. This study aims to identify the best classification model to classify COVID-19 by using significant weather features chosen by Principle Component Analysis (PCA) feature selection method. The DS4C COVID-19 dataset is used to train and validate each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by PCA. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized CNN classifier with seven weather variables selected by PCA achieved the highest performance among all the techniques. The experimental results obtained show that the weather variables are more relevant in predicting the confirmed cases as compared to the other variables. Thus, from this result, it is evident that temperature, humidity, wind and precipitation are important features for predicting COVID-19 confirmed cases.

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“…A study performed by [18] compared Bayes Net, Naive Bayes, and a hybrid of these two classifiers to see which one will yield the better results using diabetic patients' data accessible in the WEKA tool. The results suggest that combining Bayes Net and Naive Bayes produces better results than using these classifiers independently.…”

Section: A Application Of Naï Ve Bayes Algorithmmentioning

confidence: 99%

Predicting Student Program Completion Using Naïve Bayes Classification Algorithm

Perez¹,

Perez²

2021

IJMECS

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Data mining approaches provide different educational institutions opportunities to find hidden patterns from the data stored in the database. Many researchers have used these data to develop a model that would assist the institution administrators in decision-making. This study was performed to predict student program completion using the Naï ve Bayes classifier technique. The dataset utilized in this study was obtained from Bulacan State University -Sarmiento Campus in the Philippines under BS Information Technology program from five-year graduates' data for Academic Year 2012-2016. This dataset was pre-processed, cleansed, transformed, and balanced before constructing the model. Ten predictors were used for predicting student completion. The feature selection technique was used to filter and evaluate the significance of each factor. The significant variables assessed by the feature selection technique (Weight by Correlation) were the final parameters in creating the model. The Naï ve Bayes classifier was applied to predict the students' completion using the 70:30 ratios for training and testing dataset distribution. Correlation analysis identified the weight of individual attributes to the label attribute. From 10 possible predictor variables, only four (4) predictor variables were selected after correlation analysis. The identified significant attributes affecting program completion, namely (in order of significance): parents' monthly income, mother and father's educational attainment, and High School GPA attributes. The significant attributes identified in correlation analysis splitted into 70% training data or 447 records and 30% testing data or 191 records. There were 84 out of 191 data samples, or 44% of students were predicted to complete the program. On the other hand, 107 out of 191 data samples, or 56%, were predicted as not completing the program. The accuracy values performed an 84% rating with 80.46% class precision, and 83.33% class recall in the testing dataset (n=191). The outcomes of this study have a significant impact on HEIs, particularly on college completion rates. This study shall be highly significant and beneficial specifically to university administrators as this be a tool for them to identify students who will complete college based on variables included in the model.

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Comparative Analysis of Bayes Net Classifier, Naive Bayes Classifier and Combination of both Classifiers using WEKA

Cited by 8 publications

References 15 publications

Pengembangan Layanan Kependudukan Dan Pencatatan Sipil Menggunakan Algoritma Naïve Bayes

Pengembangan Layanan Kependudukan Dan Pencatatan Sipil Menggunakan Algoritma Naïve Bayes

Predicting COVID-19 Based on Environmental Factors With Machine Learning

Predicting Student Program Completion Using Naïve Bayes Classification Algorithm

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