A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

Weldegebriel, Halefom Tekle; Liu, Ran; Haq, Anwar Ul; Bugingo, Emmanuel; Zhang, Defu

doi:10.1109/access.2019.2960161

Cited by 39 publications

(20 citation statements)

References 23 publications

(22 reference statements)

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“…The Extreme Gradient Boosting (XGBoost) is a decision-tree-based ensemble machine learning algorithm that uses a gradient boosting framework. As we said before, an ensemble method is a machine learning technique that combines several base models to produce one optimal predictive model (Weldegebriel et al 2020). An algorithm is called boosting if it works by adding models on top of each other iteratively, the errors of the previous model are corrected by the next predictor until the training data is accurately predicted or reproduced by the model.…”

Section: Extreme Gradient Boosting Classifiermentioning

confidence: 99%

Forecasting mid-price movement of Bitcoin futures using machine learning

et al. 2021

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In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

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Section: Extreme Gradient Boosting Classifiermentioning

confidence: 99%

Forecasting mid-price movement of Bitcoin futures using machine learning

et al. 2021

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“…Among the learning-based methods, the convolutional neural network is a typical deep learning model. Due to its shared structure of local receptive fields and weights, the model has strong learning ability and does not require artificial prior knowledge [ 25 – 27 ]. Figure 1 shows hierarchical distribution of convolutional neural network.…”

Section: Construction Of a Classification Model Of Porcelain Fragment Texture Images Based On Convolutional Neural Networkmentioning

confidence: 99%

[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

2021

Computational Intelligence and Neuroscience

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The texture image decomposition of porcelain fragments based on convolutional neural network is a functional algorithm based on energy minimization. It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet-based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. The production of big data that meets the characteristics of the background text is actually an image-based normalization method. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. A super-resolution model for certain texture features can improve the restoration effect of such texture images. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg/csv two standardized database datasets after segmentation. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future.

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“…[1] where the authors use shallow NNs as base learners in the GBM. The same combination of the NN and XGBoost was proposed by Weldegebriel et al [30]. Ideas of the NN and GBM combination have been also studied by other authors, for example, [2,20].…”

Section: Introductionmentioning

confidence: 90%

A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines

Konstantinov¹,

Utkin²

2020

Preprint

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The gradient boosting machine is one of the powerful tools for solving regression problems. In order to cope with its shortcomings, an approach for constructing ensembles of gradient boosting models is proposed. The main idea behind the approach is to use the stacking algorithm in order to learn a second-level meta-model which can be regarded as a model for implementing various ensembles of gradient boosting models. First, the linear regression of the gradient boosting models is considered as a simplest realization of the meta-model under condition that the linear model is differentiable with respect to its coefficients (weights). Then it is shown that the proposed approach can be simply extended on arbitrary differentiable combination models, for example, on neural networks which are differentiable and can implement arbitrary functions of gradient boosting models. Various numerical examples illustrate the proposed approach.

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A New Hybrid Convolutional Neural Network and eXtreme Gradient Boosting Classifier for Recognizing Handwritten Ethiopian Characters

Cited by 39 publications

References 23 publications

Forecasting mid-price movement of Bitcoin futures using machine learning

Forecasting mid-price movement of Bitcoin futures using machine learning

[Retracted] Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network

A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines

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