Evaluation of Decision Tree, K-NN, Naive Bayes and SVM with MWMOTE on UCI Dataset

Untoro, Meida Cahyo; Praseptiawan, Mugi; Widianingsih, Mastuti; Ashari, Ilham Firman; Afriansyah, Aidil; Oktafianto,

doi:10.1088/1742-6596/1477/3/032005

Cited by 14 publications

(13 citation statements)

References 11 publications

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“…Jadhav and Channe 47 , compared between decision tree, K-NN and Naïve Bayes classifier by using whether data and concluded that the accuracy of decision tree and KNN was more accurate (99%) compared to Naïve Bayes classifier which had the accuracy of 92.857%. Untoro et al 48 compared the Decision Tree, K-NN, Naive Bayes and SVM with MWMOTE on UCI Dataset and found that decision tree is an efficient process compared to K-NN, SVM and Naïve Bayes and concluded that for Decision Tree test data had an accuracy value of 94.32%, KNN of 92.67%, Support Vector Machine of 85.61%, and Naïve Bayes of 84.30%. Yadav et al 49 compared the fish abundance prediction accuracy between linear regression, neural networks and classification and regression tree (CART) models and found that NNs and CART models produced better prediction accuracy compared to LR model.…”

Section: Resultsmentioning

confidence: 99%

IoT and ML approach for ornamental fish behaviour analysis

Patro,

Yadav,

Bharti

et al. 2023

Sci Rep

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Ornamental fish keeping is the second most preferred hobby in the world and it provides a great opportunity for entrepreneurship development and income generation. Controlling the environment in ornamental fish farm is a considerable challenge because it is affected by a variety of parameters like water temperature, dissolved oxygen, pH, and disease occurrences. One particular interesting ornamental fish species is goldfish (Carassius auratus). Machine learning (ML) and deep learning technique have significant potential in analysing voluminous data collected from fish farm. Through this technique, the fish farmers can get insight on feeding behaviour, fish growth patterns, predict diseases/stress, and environmental factors affecting fish health. The aim of the study is to analyze the behavioural changes in goldfish due to alterations in environmental parameters (water temperature and dissolved oxygen). Decision tree, Naïve Bayes classifier, K-nearest neighbour (KNN), and linear discriminant analysis (LDA) were used to analyse the behavioural change data. To compare the performance between all four classifiers, cross validation and confusion matrix used. The cross-validation error of LDA, Naïve Bayes classification, KNN and decision tree was 19.86, 28.08, 30.14 and 13.78 respectively. Decision tree was proved to be the most accurate and effective classifier. Different temperature and DO range were taken to predict fish behaviour. Some findings are, the behaviour of fish was rest between temperature 37.85 °C and 40.535 °C, erratic when temperature was greater than or equal to 40.535 °C, gasping when temperature was between 37.85 and 40.535 °C and when DO concentration was less than 6.58 mg/L. Blood parameter analysis has been done to validate the change in external behaviours with change in physiological parameters.

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Section: Resultsmentioning

confidence: 99%

IoT and ML approach for ornamental fish behaviour analysis

Patro,

Yadav,

Bharti

et al. 2023

Sci Rep

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“…There are many types of algorithms for classifying sentiment, one of which is Support Vector Machine (SVM) [15]. SVM in several studies achieved higher accuracy [12] [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis of the Minister of Education and Culture using Vader and RBF, Polynomial, Linier Kernels SVM Based on Binary Particle Swarm Optimization

Sinaga,

Ashari,

Yulita

2024

SISTEMASI

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Comments from social media can be analyzed further. Social media is used to interact from one person to another, as well as with the government. This Issue Was Raised Because Of Debate And Public Opinion From The Community, Institutions And Ngos Regarding Ministerial Regulation No. 30 Of 2021 Concerning Prevention And Handling Of Sexual Violence. In The Higher Education Environment, Therefore In This Research We Want To Examine What Is The Main Root Of The Problem Using A Methodical Approach Using Natural Language Processing. The pre-processing applied is case folding, tokenization, elimination of stop words, stemming using literature. The model implementing PSO failed to improve accuracy on all kernels. Best performance before applying PSO to twitter dataset using linear kernel. This study conducted sentiment analysis regarding the issuance of ministerial regulation no. 30 of 2021. The data obtained was then preprocessed. The performance measured is accuracy and f1-macro in the model without PSO and accuracy in the model using accuracy. The model to be formed uses linear kernels, RBF and polynomials of order 1 and order 2. Sentence analysis is a field that analyzes sentiment, attitudes and emotions of entities and their attributes in text form. The aim of this research is to compare the performance of the Support Vector Machine classification algorithm without Particle Swarm Optimization feature selection and the performance of the Support Vector Machine classification algorithm using Particle Swarm Optimization feature selection. The data obtained is then pre-processed. The data set was automatically labeled using VADER (Valence Dictionary for Sentiment Reasoning). The kernels that succeeded in increasing accuracy were the RBF kernel and polynomials on the Twitter dataset. Keywords: SVM, Vader, PSO, Sentiment Analysis, Government Policy

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“…Korade in [10] applied different machine learning techniques and compared them to identify the best suitable for the prediction of wine by selecting features. In [4] [17] investigated DT, KNN, NB, and SVM using MWMOTE (The Majority Weighted Minority Oversampling Technique). These suggestions outcomes enabled us to synthesize synthetic data more accurately while lowering the level of bias or noise.…”

Section: Related Workmentioning

confidence: 99%

SFC: A Sampling from Clusters for Reduction of Dataset Size

Tigga¹,

Pal²,

Mustafi³

2023

Preprint

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Since managing enormous datasets in the real world is difficult, it is necessary to minimize the size of the data set, so that the accuracy of the original dataset is no longer impacted. In this study, the categorization of the white wine dataset is examined using a number of machine learning techniques, including Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), K Nearest Neighbour (KNN), and Logistic Regression (LR). Additionally, we utilized the stated dataset using the defined methodologies and presented the Sampling from Clusters (SFC) approach. The white wine dataset is first clustered using our suggested method SFC, and then 95% of the data from each cluster is removed and combined to create a standard dataset for classification process. For 90%, 85%, and 80% of the data, the same procedure is repeated. On the other hand, we used a random sampling (RS) technique to work with 95% of the data from the dataset in question, and we compared the results with SFC using evaluation metrics like accuracy, precision, recall, F1-score, Receiver Operating Characteristic (ROC), Area under the Curve (AUC), binomial confidence interval (CI), and MSE. With 90%, 85%, and 80% of the datasets, the same procedure is repeated. According to statistics, confidence intervals CI become tighter as the quantity of test data N increases; they range from 0.72 to 0.76 for NB, 0.73 to 0.79 for SVM, 0.82 to 0.86 for RF, 0.75 to 0.77 for KNN, and 0.74 to 0.80 for LR.

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Evaluation of Decision Tree, K-NN, Naive Bayes and SVM with MWMOTE on UCI Dataset

Cited by 14 publications

References 11 publications

IoT and ML approach for ornamental fish behaviour analysis

IoT and ML approach for ornamental fish behaviour analysis

Sentiment Analysis of the Minister of Education and Culture using Vader and RBF, Polynomial, Linier Kernels SVM Based on Binary Particle Swarm Optimization

SFC: A Sampling from Clusters for Reduction of Dataset Size

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