Results 31 to 40 of about 36,115 (166)

Optimasi MWMOTE pada data tidak seimbang menggunakan complete linkage

Jurnal Teknologi dan Sistem Komputer, 2021
Data yang tidak seimbang dapat menyebabkan kesalahan klasifikasi, menurunkan kinerja dan akurasi. Pengelompokan pada MWMOTE dapat dioptimalkan untuk meningkatkan kinerja pembangkitan data sintetis menjadi representatif serta meningkatkan kinerja MWMOTE ...
Meida Cahyo Untoro
doaj   +1 more source

Hybrid oversampling technique for imbalanced pattern recognition: Enhancing performance with Borderline Synthetic Minority oversampling and Generative Adversarial Networks

Machine Learning with Applications
Class imbalance problems (CIP) are one of the potential challenges in developing unbiased Machine Learning models for predictions. CIP occurs when data samples are not equally distributed between two or multiple classes.
Md Manjurul Ahsan, Shivakumar Raman, Yingtao Liu, Zahed Siddique   +3 more
doaj   +1 more source

Digital Self-Interference Cancellation Based on Blind Source Separation and Spectral Efficiency Analysis for the Full-Duplex Communication Systems

IEEE Access, 2018
In this paper, the fundamental problem for the full-duplex communication systems, i.e., self-interference cancellation (SIC), is investigated, and a novel digital-domain SIC method based on blind source separation is proposed. This method achieves SIC by
Hua Yang, Hang Zhang, Jiang Zhang, Liu Yang   +3 more
doaj   +1 more source

The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling

IEEE Access, 2020
In this paper, we propose a new metric to measure goodness-of-fit for classifiers: the Real World Cost function. This metric factors in information about a real world problem, such as financial impact, that other measures like accuracy or F1 do not. This
Yaoshiang Ho, Samuel Wookey
doaj   +1 more source

Adaptive equalization in oversampled subbands [PDF]

Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), 1998
The potential presence of fractional delays, nonminimum phase parts, and a colouring of the channel output can require adaptive equalisers to adapt very long filters, which can have slow convergence for LMS-type adaptive algorithms. The authors present a novel oversampled subband approach to adaptive equalisation, which can both significantly reduce ...
Weiss, Stephan, Dooley, Saul, Stewart, Robert, Asoke, Nandi   +3 more
openaire   +7 more sources

Handling Class Imbalanced Data in Sarcasm Detection with Ensemble Oversampling Techniques

Applied Artificial Intelligence
The rise of social media has amplified online sharing, necessitating businesses to comprehend public sentiment. Traditional sentiment analysis struggles with sarcasm detection and class imbalance.
Ya-Han Hu, Ting-Hsuan Liu, Chih-Fong Tsai, Yu-Jung Lin   +3 more
doaj   +1 more source

Multiscale-Spectral GFEM and optimal oversampling [PDF]

Computer Methods in Applied Mechanics and Engineering, 2020
In this work we address the Multiscale Spectral Generalized Finite Element Method (MS-GFEM) developed in [I. Babuška and R. Lipton, Multiscale Modeling and Simulation 9 (2011), pp. 373--406]. We outline the numerical implementation of this method and present simulations that demonstrate contrast independent exponential convergence of MS-GFEM solutions.
Ivo Babuska, Robert Lipton 0001, Paul Sinz, Michael Stuebner   +3 more
openaire   +3 more sources

Early Detection of Asymptomatic Covid-19 Infection with Artificial Neural Network Model Through Voice Recording of Forced Cough

JOIV: International Journal on Informatics Visualization, 2023
SARS-CoV-2 is a virus that spreads the infection known as COVID-19, or Coronavirus 2019. According to data from the World Health Organization as of March 15, 2021, Indonesia has 1,419,455 cumulative cases and 38,426 cumulative deaths, ranking third among
Aisyah Khairun Nisa, I Gede Pasek Suta Wijaya, Arik Aranta   +2 more
doaj   +1 more source

LoRAS: an oversampling approach for imbalanced datasets [PDF]

Machine Learning, 2020
AbstractThe Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model.
Saptarshi Bej, Narek Davtyan, Markus Wolfien, Mariam Nassar, Olaf Wolkenhauer   +4 more
openaire   +3 more sources

MWMOTE-FRIS-INFFC: An Improved Majority Weighted Minority Oversampling Technique for Solving Noisy and Imbalanced Classification Datasets

Applied Sciences
In view of the data of fault diagnosis and good product testing in the industrial field, high-noise unbalanced data samples exist widely, and such samples are very difficult to analyze in the field of data analysis.
Dong Zhang, Xiang Huang, Gen Li, Shengjie Kong, Liang Dong   +4 more
doaj   +1 more source