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Effect of Giving N Value on ADA N Method for Classification of Imbalanced Nominal Data


contributor author Sri Rahayu, Jeffry Andhika Putra, MZ Yumarlin
date accessioned Sun, 12 April 2026
date available Sun, 12 April 2026
date issued Rabu, 20 November 2019
description abstract Class imbalances in data mining research are quite detrimental because there are difficulties in classifying minority classes (small number of instances) correctly. Oversampling is a method of balancing class distribution by randomly replicating instances in minority classes. multiclass classification might achieve a lower performance than binary classification as the boundaries among the classes may overlap. This issue may become more complex when facing imbalanced data. This study presents test results giving different k values in nearest neighbor searches which are used to balance the class using the ADASYN-N method. The results show that giving different k values can affect the performance of the ADASYN-N method depending on the number of dataset instances.
subject Effect of Giving N Value on ADA N Method for Classification of Imbalanced Nominal Data
title Effect of Giving N Value on ADA N Method for Classification of Imbalanced Nominal Data
type

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