首页|Research Reports from Telkom University Provide New Insights into Support Vector Machines (Kmeans-SMOTE Integration for Handling Imbalance Data in Classifying F inancial Distress Companies using SVM and Naive Bayes)
Research Reports from Telkom University Provide New Insights into Support Vector Machines (Kmeans-SMOTE Integration for Handling Imbalance Data in Classifying F inancial Distress Companies using SVM and Naive Bayes)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on support vector machines are presented in a new report. According to news reporting from Telkom Universit y by NewsRx journalists, research stated, “Imbalanced data presents significant challenges in machine learning, leading to biased classification outcomes that f avor the majority class.” Our news reporters obtained a quote from the research from Telkom University: “T his issue is especially pronounced in the classification of financial distress, where data imbalance is common due to the scarcity of such instances in real-wor ld datasets. This study aims to mitigate data imbalance in financial distress co mpanies using the Kmeans-SMOTE method by combining Kmeans clustering and the syn thetic minority oversampling technique (SMOTE). Various classification approache s, including Nave Bayes and support vector machine (SVM), are implemented on a K aggle financial distress data set to evaluate the effectiveness of Kmeans-SMOTE. Experimental results show that SVM outperforms Nave Bayes with impressive accur acy (99.1%), f1-score (99.1%), area under precision re call (AUPRC) (99.1%), and geometric mean (Gmean) (98.1% ).”
Telkom University, Machine Learning, Sup port Vector Machines
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