Gradually Generative Adversarial Networks Method for Imbalanced Datasets

Abstract

Imbalanced dataset can cause obstacles to classification and result in a decrease in classification performance. There are several methods that can be used to deal the data imbalances, such as methods based on SMOTE and Generative Adversarial Networks (GAN). These methods are used for overcoming data oversampling so that the amount of minority data can increase and it can reach a balance with the majority data. In this research, the selected dataset is classified as a small imbalanced dataset of less than 200 records. The proposed method is the Gradually Generative Adversarial Network (GradGAN) model which aims to handle data imbalances gradually. The stages of the GradGAN model are adding the original minority dataset gradually so that it will create new minority datasets until a balance of data is created. Based on the algorithm flow described, the minority data is multiplied by the value of the variable that has been determined repeatedly to produce new balanced minority data. The test results on the classification of datasets from the GradGAN model produce an accuracy value of 8.3% when compare to that without GradGAN.

Komparasi Metode Mean dan KNN Imputation dalam Mengatasi Missing Value pada Dataset Kecil

“…Dalam metode imputasi Mean prosesnya mengisi data yang memiliki nilai yang hilang dan mendapatkan data yang lengkap dengan jumlah dari perhitungan rata-rata dari data yang tidak hilang. Metode imputasi KNN Imputation prosesnya menentukan jumlah tetangga terdekat yang disimbolkan dengan k, kemudian menghitung jarak terkecil dari setiap observasi yang tidak mengandung missing data pada dataset kecil (Misdram et al, 2023).…”