Ensemble approaches for regression: A survey
The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …
prediction accuracy in learning problems with a numerical target variable. The process of …
Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting
Load forecasting implies directly in financial return and information for electrical systems
planning. A framework to build wavenet ensemble for short-term load forecasting is …
planning. A framework to build wavenet ensemble for short-term load forecasting is …
Feature-weighted linear stacking
Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending
the predictions of multiple machine learning models. Recent work has shown that the use of …
the predictions of multiple machine learning models. Recent work has shown that the use of …
Diversity in search strategies for ensemble feature selection
Ensembles of learnt models constitute one of the main current directions in machine
learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not …
learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not …
Diagnosis of diabetes type-II using hybrid machine learning based ensemble model
The work done in this paper exhibits an expert system based ensemble model in diagnosing
type-II diabetes. Diabetes Mellitus is a disease with high mortality rate that affects more than …
type-II diabetes. Diabetes Mellitus is a disease with high mortality rate that affects more than …
A dynamic gradient boosting machine using genetic optimizer for practical breast cancer prognosis
This research proposes a novel genetic algorithm-based online gradient boosting (GAOGB)
model for incremental breast cancer (BC) prognosis. The development of clinical information …
model for incremental breast cancer (BC) prognosis. The development of clinical information …
Ensemble feature selection with the simple Bayesian classification
A Tsymbal, S Puuronen, DW Patterson - Information fusion, 2003 - Elsevier
A popular method for creating an accurate classifier from a set of training data is to build
several classifiers, and then to combine their predictions. The ensembles of simple Bayesian …
several classifiers, and then to combine their predictions. The ensembles of simple Bayesian …
Explainable AI for industry 4.0: semantic representation of deep learning models
V Terziyan, O Vitko - Procedia Computer Science, 2022 - Elsevier
Artificial Intelligence is an important asset of Industry 4.0. Current discoveries within machine
learning and particularly in deep learning enable qualitative change within the industrial …
learning and particularly in deep learning enable qualitative change within the industrial …
Dynamic integration of regression models
In this paper we adapt the recently proposed Dynamic Integration ensemble techniques for
regression problems and compare their performance to the base models and to the popular …
regression problems and compare their performance to the base models and to the popular …
Late deep fusion of color spaces to enhance finger photo presentation attack detection in smartphones
Finger photo recognition represents a promising touchless technology that offers portable
and hygienic authentication solutions in smartphones, eliminating physical contact. Public …
and hygienic authentication solutions in smartphones, eliminating physical contact. Public …