Journal of Physics Conference Series · 2020 · 20 citations · 5 references
Search OptimizationEngineeringMachine LearningMany AttributesPathologyAbstract DataComputational MedicineOptimization-based Data MiningClassification MethodData ScienceData MiningPattern RecognitionBiostatisticsPublic HealthInstance-based LearningKnowledge DiscoveryBioinformaticsK-nearest Neighbor AlgorithmGain RatioData ClassificationClassificationClassifier SystemSimilarity SearchHealth Informatics
Abstract Data that has many attributes or higher dimensions will affect the performance of the K-NN classification algorithm. In this study, the Gain Ratio implemented for selecting and reducing the dataset attributes to form a new dataset for the classification process is carried out with the K-NN. The dataset used in this study are the Breast Cancer Coimbra dataset and Hepatitis C Virus dataset obtained from the UCI Machine Learning Repository. The results showed that the Breast Cancer Coimbra dataset, Gain Ratio can improve the performance of K-NN with average value 0.535596 TPR, TNR = 1, NPV = 0.608279, FNR = 1, FOR = 0.391721, Accuracy = 72.85%. In Hepatitis C Virus dataset also managed to improve the performance of K-NN with average value TPR = 0.665596, TNR = 0,876667, NPV=0,738279, FNR=0,88, FOR=0,521721, and Accuracy=86,25%.
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