Data Classification based on Decision Tree, Rule Generation, Bayes and Statistical Methods: An Empirical Comparison

Saha, Sanjib; Nandi, Debashis

doi:10.5120/ijca2015906891

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…The value of P ( 1 … ) is constant for each experiment so that the maximum value of a class is determined by the maximum value between P (A) P ( 1 … | A) [15]. The equation function formed becomes a maximum multiplication for the prior value and the likelihood function, the function is shown in Equation 3.…”

Section: Naïve Bayes Methodsmentioning

confidence: 99%

“…From the training data set that is applied to the model, the confusion matrix produces several values, namely Accuracy, Precision, Recall, and F-Measure. Accuracy is the percentage of cases that have predictions and real values are both positive (TP) orboth negative (TN) compared to the total number of cases [15]. Precision or confidence is the ratio between cases that have predictions and real values are both positive (TP) compared to the overall positive predicted cases (TP + FP).…”

Section: Confusion Matrix Testingmentioning

confidence: 99%

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Estimation System For Late Payment Of School Tuition Fees

Muqorobin

Kusrini

Rokhmah

et al. 2020

IJCIS

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The Surakarta Al-Islam Vocational School is a private educational institution that requires all students to pay school tuition fees. Education is an obligation for all Indonesian citizens. The cost of education is one of the most important input components in implementing education. Because cost is the main requirement in achieving educational goals. SPP School is a routine school fee that is carried out every month. Based on last year's School Admin report, many students were late in paying school tuition fees, around 60%. This is a very big problem because the income of school funds comes from school tuition. The purpose of this research is that the researcher will build a prediction system using the best classification method, which is to compare the accuracy level of the Naïve Bayes method with the K-K-Nearest Neighbor method. Because both methods can make class classifications right or late, in paying school fees. processing using dapodic data for 2017/2018 as many as 236 data. In improving accuracy, the researcher also applies feature selection with Information Gain, which is useful for selecting optimal parameters. System testing is carried out using the Confusion Matrix method. The final results of this study indicate that the Naïve Bayes Method + Information Gain Method produces the highest accuracy, namely 95% compared to the Naïve Bayes method alone, namely 85% and the K-NN method, namely 81%.

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Section: Naïve Bayes Methodsmentioning

confidence: 99%

Section: Confusion Matrix Testingmentioning

confidence: 99%

Estimation System For Late Payment Of School Tuition Fees

Muqorobin

Kusrini

Rokhmah

et al. 2020

IJCIS

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show abstract

“…For Regression Classification numerical attribute datasets, NBTree and multiclass Classifier are the best methods. For the categorical attributes of the NB-Tree dataset, Classification via Regression and Bayes Net methods is best [9]. Of these above five rules classification method based on PART and Decision Tree method is the best.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

Saputra

Irmayani

Purwaningtias

et al. 2021

Int. J. Adv. Data Inf. Syst.

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Heart disease is a general term for all of types of the disorders which is affects the heart. This research aims to compare several classification algorithms known as the C4.5 algorithm, Naïve Bayes, and Support Vector Machine. The algorithm is about to optimize of the heart disease predicting by applying Particle Swarm Optimization (PSO). Based on the test results, the accuracy value of the C4.5 algorithm is about 74.12% and Naïve Bayes algorithm accuracy value is about 85.26% and the last the Support Vector Machine algorithm is about 85.26%. From the three of algorithms above then continue to do an optimization by using Particle Swarm Optimization. The data is shown that Naïve Bayes algorithm with Particle Swarm Optimization has the highest value based on accuracy value of 86.30%, AUC of 0.895 and precision of 87.01%, while the highest recall value is Support Vector Machine algorithm with Particle Swarm Optimization of 96.00%. Based on the results of the research has been done, the algorithm is expected can be applied as an alternative for problem solving, especially in predicting of the heart disease.

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“…In the paper [7], twenty well known data mining classification methods are applied on ten UCI machine learning medical datasets, and the performance of various classification methods are empirically compared while varying the number categorical and numeric attributes. The types of attributes and the number of instances in datasets.…”

Section: Related Workmentioning

confidence: 99%