ArticleComputational intelligence and neuroscience2017
Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines.
Article in Computational intelligence and neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Red fescue (Festuca rubra L.) variety recognition using subset division and neural networks.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- A novel hybrid CCD-ML approach for predicting permeability alterations in carbonate reservoir rocks during waterflooding under scale inhibitor treatment.Scientific reports · 2026Article
- Fast blood flow index reconstruction of diffuse correlation spectroscopy using a back-propagation-free data-driven algorithm.Biomedical optics express · 2025Article
- Age-group determination of living individuals using first molar images based on artificial intelligence.Scientific reports · 2021Article
- Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2019Article
- Machine Learning Model for Imbalanced Cholera Dataset in Tanzania.TheScientificWorldJournal · 2019Article
- SGB-ELM: An Advanced Stochastic Gradient Boosting-Based Ensemble Scheme for Extreme Learning Machine.Computational intelligence and neuroscience · 2018Article
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Authors and funding
5 authors.
Funding
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Abstract
Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM,
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.