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Imbalanced Data Classification Method Based on LSSASMOTE
IEEE Access, 2023 Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper ...
Zhi Wang, Qicheng Liu
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Joint optimization of transceivers with fractionally spaced equalizers [PDF]
, 2008 In this paper we propose a method for joint optimization of transceivers with fractionally spaced equalization (FSE). We use the effective single-input multiple-output (SIMO) model for the fractionally spaced receiver.
Vaidyanathan, P. P., Weng, Ching-Chih
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Oversampling generates super-wavelets [PDF]
Proceedings of the American Mathematical Society, 2007 We show that the second oversampling theorem for affine systems generates super-wavelets. These are frames generated by an affine structure on the space L 2 ( R d
Dutkay, Dorin Ervin, Jorgensen, Palle
openaire +3 more sources
A Review on Oversampling Techniques for Solving the Data Imbalance Problem in Classification
International Journal on Advances in ICT for Emerging Regions (ICTer), 2023 The data imbalance problem is a widely explored area in the Machine Learning domain. With the rapid advancement of computing infrastructure and the incessant increase in the amount and variety of data generated, the data imbalance problem has prevailed ...
T. Piyadasa, K. Gunawardana
semanticscholar +1 more source
Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics [PDF]
, 2011 We study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity.
Lu, Yue M. +3 more
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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
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Machine Learning with ApplicationsClass 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 +3 more
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Over-sampling imbalanced datasets using the Covariance Matrix [PDF]
EAI Endorsed Transactions on Energy Web, 2020 INTRODUCTION: Nowadays, many machine learning tasks involve learning from imbalanced datasets,leading to the miss-classification of the minority class. One of the state-of-the-art approaches to ”solve” thisproblem at the data level is Synthetic Minority ...
Ireimis Leguen-deVarona +3 more
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Scientific Programming, 2022 Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the body due to impaired contractile properties of the muscles of the heart.
Mirza Muntasir Nishat +7 more
semanticscholar +1 more source
A 13-bit, 2.2-MS/s, 55-mW multibit cascade ΣΔ modulator in CMOS 0.7-μm single-poly technology [PDF]
, 1999 This paper presents a CMOS 0.7-μm ΣΔ modulator IC that achieves 13-bit dynamic range at 2.2 MS/s with an oversampling ratio of 16. It uses fully differential switched-capacitor circuits with a clock frequency of 35.2 MHz, and has a power consumption of ...
Medeiro Hidalgo, Fernando +2 more
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