[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
Survey on synthetic data generation, evaluation methods and GANs
A Figueira, B Vaz - Mathematics, 2022 - mdpi.com
Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …
quality, synthetic data can be used, for example, to improve the performance of machine …
[PDF][PDF] Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM: Comparative Evaluation of SVM Kernels for Sentiment …
S Rabbani, D Safitri, N Rahmadhani… - … : Indonesian Journal of …, 2023 - journal.irpi.or.id
Abstract Kebijakan perubahan harga Bahan Bakar Minyak (BBM) oleh pemerintah pada
September 2022 lalu menimbulkan kontroversi pengguna sosial media termasuk Twitter …
September 2022 lalu menimbulkan kontroversi pengguna sosial media termasuk Twitter …
A comparison of undersampling, oversampling, and SMOTE methods for dealing with imbalanced classification in educational data mining
Educational data mining is capable of producing useful data-driven applications (eg, early
warning systems in schools or the prediction of students' academic achievement) based on …
warning systems in schools or the prediction of students' academic achievement) based on …
Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection
With the continuous emergence of various network attacks, it is becoming more and more
important to ensure the security of the network. Intrusion detection, as one of the important …
important to ensure the security of the network. Intrusion detection, as one of the important …
Bias in machine learning software: Why? how? what to do?
Increasingly, software is making autonomous decisions in case of criminal sentencing,
approving credit cards, hiring employees, and so on. Some of these decisions show bias …
approving credit cards, hiring employees, and so on. Some of these decisions show bias …
A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance
D Elreedy, AF Atiya - Information Sciences, 2019 - Elsevier
Imbalanced classification problems are often encountered in many applications. The
challenge is that there is a minority class that has typically very little data and is often the …
challenge is that there is a minority class that has typically very little data and is often the …
Resampling imbalanced data for network intrusion detection datasets
S Bagui, K Li - Journal of Big Data, 2021 - Springer
Abstract Machine learning plays an increasingly significant role in the building of Network
Intrusion Detection Systems. However, machine learning models trained with imbalanced …
Intrusion Detection Systems. However, machine learning models trained with imbalanced …
On the class overlap problem in imbalanced data classification
Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …
existing and recent literature showed that class overlap had a higher negative impact on the …
Heart sound classification based on improved MFCC features and convolutional recurrent neural networks
Heart sound classification plays a vital role in the early detection of cardiovascular disorders,
especially for small primary health care clinics. Despite that much progress has been made …
especially for small primary health care clinics. Despite that much progress has been made …