Naive Bayes with Correlation Factor for Text Classification Problem

Chen, Jiangning; Dai, Zhibo; Duan, Juntao; Matzinger, Heinrich; Popescu, Ioan-Iovitz

doi:10.1109/icmla.2019.00177

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…Rule-based classifiers merupakan salah satu teknik klasifikasi dengan menggunakan aturan "if ... then ... else..." [8]. Bayesian classifiers menggunakan metode statistik dan berdasarkan pada teori Bayes [9]. Artificial Neural Networks merupakan metode klasifikasi dengan menggunakan perhitungan yang mengadaptasi cara kerja otak manusia [10].…”

Section: Pendahuluanunclassified

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Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

Azhari¹,

Situmorang

Rosnelly³

2021

mib

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In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm

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Section: Pendahuluanunclassified

“…Algoritme Naïve Bayes merupakan sebuah metode klasifikasian yang menerapkan teorema Bayes yang berdasarkan nilai probabilitas [9]. Jika terdapat dua buah kejadian A dan kejadian B dalam suatu kondisi maka nilai probabilitas kejadian B terhadap kejadian B dapat di rumuskan dengan persamaan berikut:…”

Section: Algoritma Naive Bayesunclassified

Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

Azhari¹,

Situmorang

Rosnelly³

2021

mib

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“…Once the data is collected, carried out the data processing through data cleansing, data integration, data selection, data transformation [24], and the establishment of the dataset that will be used as training data and testing data [25], [26].…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

Measure the effectiveness of information systems with the naïve bayes classifier method

Triayudi

Sumiati

Dwiyatno

et al. 2021

IJ-AI

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<span id="docs-internal-guid-a31a7709-7fff-65df-d360-f52175b11d47"><span>Technological advances at this time are developing very fast, information systems became the frontline in technological advancements, the need for information systems to support jobs is increasingly high. However, its implementation for users does not have a significant impact, so that it needs to be reviewed and re-evaluated in the use of the information system built. The naive bayes classifier method can provide "effective" and "ineffective" conclusions and is used as material for evaluation and improvement. The purpose of this study is to contribute to measuring the effectiveness of the information system, to solve problems with the naïve bayes classifier method approach which has advantages in the process of classifying data and predicting data. From the test results three times, training has been conducted using 100 data, accuracy value of 84.82% and error 15.18%.</span></span>

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“…Naïve Bayes' algorithm; which gives the probability of the prediction, in knowledge of previous events [77][78][79][80][81][82]. Clustering is always using mathematics; we will group the data into packets so that in each packet the data is as close as possible to each other [83][84][85]. It is used in particular in recommendations for films close to the films we have already seen!…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence Approach for Target Classification: A State of The Art

Abdellaoui¹,

Daghouj²,

Fattah³

et al. 2020

Adv. sci. technol. eng. syst. j.

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The classification of static or mobile objects, from a signal or an image containing information as to their structure or their form, constitutes a constant concern of specialists in the electronic field. The remarkable progress made in past years, particularly in the development of neural networks and artificial intelligence systems, has further accentuated this trend. The fields of application and potential uses of Artificial Intelligence are increasingly diverse: understanding of natural language, visual recognition, robotics, autonomous system, Machine learning, etc. This paper is a state of the art on the classification of radar signals. It focuses on the contribution of artificial intelligence to the latter without forgetting target tracking. This by evoking the different feature extractors, classifiers and the existing identification deep learning algorithms. We detail also the process allowing carrying out this classification.

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Naive Bayes with Correlation Factor for Text Classification Problem

Cited by 14 publications

References 13 publications

Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

Measure the effectiveness of information systems with the naïve bayes classifier method

Artificial Intelligence Approach for Target Classification: A State of The Art

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