Results 41 to 50 of about 106,398 (294)

Developing and Preliminary Testing of a Machine Learning-Based Platform for Sales Forecasting Using a Gradient Boosting Approach

Applied Sciences, 2022
Organizations engaged in business, regardless of the industry in which they operate, must be able to extract knowledge from the data available to them. Often the volume of customer and supplier data is so large, the use of advanced data mining algorithms
Antonio Panarese, Giuseppina Settanni, Valeria Vitti, Angelo Galiano   +3 more
doaj   +1 more source

GWO-XGB: Grey Wolf Optimization-based eXtreme Gradient Boosting for Hypertension Prediction in Bangladesh

, 2021
Hypertension is rapidly increasing day by day worldwide as well as in Bangladesh. The majority of people in our country die due to hypertension. So, early prediction of this disease is a very important task that may reduce the number of affected patients.
Shahriar Sikder, Tuli, Anamika Biswas, Md. Ashfikur Rahman, Md. Abdul Awal, Rahman, Md Ashfikur, Bulbul, Abdullah Al-Mamun, Tahsin, Tasfia, Sikder, Shahriar, Awal, Md Abdul, Tasfia Tahsin, Khondoker Mirazul Mumenin, Abdullah Al-Mamun Bulbul, Mumenin, Khondoker Mirazul, Pinki, Farhana Tazmim, Anamika Biswas Tuli, Farhana Tazmim Pinki   +15 more
core   +1 more source

Machine Learning-Based Forecasting of Bitcoin Price Movements

Proceedings of the International Conference on Applied Innovations in IT
In the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks,
Darko Angelovski, Bojana Velichkovska, Goran Jakimovski, Danijela Efnusheva, Marija Kalendar   +4 more
doaj   +1 more source

Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study [PDF]

Einstein (São Paulo)
Objective: Logistic Regression has been used traditionally for the development of most predictor tools of intensive care unit mortality. The purpose of this study is to combine shared risk factors between patients undergoing cardiac surgery and intensive
Beatriz Nistal-Nuño
doaj   +1 more source

Soft Gradient Boosting Machine

CoRR, 2020
Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas. However, since the training procedure of each base learner has to take the sequential order, it is infeasible to parallelize the training process among base learners for speed-up.
Ji Feng, Yi-Xuan Xu, Yuan Jiang 0001, Zhi-Hua Zhou   +3 more
openaire   +2 more sources

A new formulation of gradient boosting

Machine Learning: Science and Technology, 2021
Abstract In the setting of regression, the standard formulation of gradient boosting generates a sequence of improvements to a constant model. In this paper, we reformulate gradient boosting such that it is able to generate a sequence of improvements to a nonconstant model, which may contain prior knowledge or physical insight about the ...
Alex Wozniakowski, Jayne Thompson, Mile Gu, Felix C. Binder   +3 more
openaire   +3 more sources

Sequential Training of Neural Networks With Gradient Boosting

IEEE Access, 2023
This paper presents a novel technique based on gradient boosting to train the final layers of a neural network (NN). Gradient boosting is an additive expansion algorithm in which a series of models are trained sequentially to approximate a given function.
Seyedsaman Emami, Gonzalo Martinez-Munoz
doaj   +1 more source

Bagging ensemble selection for regression

, 2012
Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have
Quan Sun, Pfahringer, Bernhard, Bernhard Pfahringer, Sun, Quan   +3 more
core   +1 more source

Formal concept views for explainable boosting: A lattice-theoretic framework for Extreme Gradient Boosting and Gradient Boosting Models

Intelligent Systems with Applications
Tree-based ensemble methods, such as Extreme Gradient Boosting (XGBoost) and Gradient Boosting models (GBM), are widely used for supervised learning due to their strong predictive capabilities.
Sherif Eneye Shuaib, Pakwan Riyapan, Jirapond Muangprathub   +2 more
doaj   +1 more source

REGRESSION-BASED PREDICTION OF ANXIETY SEVERITY [PDF]

Carpathian Journal of Electrical Engineering
The present study explores the use of machine learning to predict self-reported anxiety levels based on demographic, behavioral, and physiological data.
Bogdan CHIS, Diana DULF, Madalina MORAR, Eleonora POP   +3 more
doaj   +1 more source