Performance Comparison and Optimized Algorithm Classification

Saputra, Dedi; Dharmawan, Weiskhy Steven; Wahyudi, Mochamad; Irmayani, Windi; Sidauruk, Juniato; Martias,

doi:10.1088/1742-6596/1641/1/012087

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Classification is how to place specific objects into a group based on their nature. This method aims to study the different functions that describe each of the data selected into one of the predefined groups of classes [13].…”

Section: Methodsmentioning

confidence: 99%

Performance Comparison of the SVM and SVM-PSO Algorithms for Heart Disease Prediction

Saputra

Dharmawan

Irmayani³

2022

Int. J. Adv. Data Inf. Syst.

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Data analysis for datasets with very large dimensions, classification is needed to predict from large datasets, in this study compare a method for classifying large data where the data will be processed to obtain the desired data prediction information. In this study, the Support Vector Machine (SVM) is used to provide the classification results of an algorithm that will be compared with the incorporation of the Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) where the test results will be compared with the SVM classification algorithm only as a comparison algorithm. better at predicting than data sets. SVM is used as a single algorithm to see different experimental results when SVM is combined with PSO. From the experiments carried out, SVM got an Accuracy value of 81.85% and an AUC value of 0.823 while SVM-PSO got an Accuracy value of 84.81% and an AUC value of 0.898.

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Section: Methodsmentioning

confidence: 99%

Performance Comparison of the SVM and SVM-PSO Algorithms for Heart Disease Prediction

Saputra

Dharmawan

Irmayani³

2022

Int. J. Adv. Data Inf. Syst.

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“…The researchers analyzed and tested five algorithms classification using four different datasets as a tool for big data classification problems. The results of the study showed that in using the UCI knowledge base of 4 datasets to calculate AUC values, the SVM method outperformed the 4 comparative methods [14]. Kalita et al researchers designed a new framework which uses new optimization modules Knowledge Based Search (KBS) and Moth-Flame Optimization (MFO) to perform optimization and efficiently train SVMs in a dynamic environment [15].…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine based Automatic Classification Method for IoT big Data Features

Yong-Hua Xu

2023

Journal of Computers

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<p>As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation.</p> <p> </p>

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“…C.45 is a method and prediction that is very strong and very widely used. C4.5 used information gain to select the attributes that will be used for object separation [22].…”

Section: C45 Algorithmmentioning

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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Performance Comparison and Optimized Algorithm Classification

Cited by 5 publications

References 3 publications

Performance Comparison of the SVM and SVM-PSO Algorithms for Heart Disease Prediction

Performance Comparison of the SVM and SVM-PSO Algorithms for Heart Disease Prediction

Support Vector Machine based Automatic Classification Method for IoT big Data Features

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

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