Classification with ensembles and case study on functional magnetic resonance imaging
TLDR
The proposed ensemble framework consists of four stages: objectives, data preparing, model training, and model testing, which is comprehensive to design diverse ensembles and can be used for a wide variety of machine learning tasks.About:
This article is published in Digital Communications and Networks.The article was published on 2021-03-28 and is currently open access. It has received 6 citations till now. The article focuses on the topics: Magnetic resonance imaging & Functional magnetic resonance imaging.read more
Citations
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A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems
TL;DR: Key findings from the review indicate that route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management are critical to safeguarding road transportation systems.
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An Efficient, Ensemble-Based Classification Framework for Big Medical Data.
Firoz Khan,Balusupati Veera Venkata Siva Prasad,Salman Ali Syed,Imran Ashraf,Lakshmana Kumar Ramasamy +4 more
TL;DR: In this article, the authors proposed an efficient, ensemble-based classification framework for big medical data to deal with the problem of insufficient classification algorithms for handling big medical datasets, which is a complicated task in the big data age.
Journal ArticleDOI
An Efficient, Ensemble-Based Classification Framework for Big Medical Data
TL;DR: In this paper , the authors proposed an efficient, ensemble-based classification framework for big medical data to deal with the problem of insufficient classification algorithms for handling big medical datasets, which involves initially applying the preprocessing technique to remove noise, missing values, and unwanted features from big medical dataset.
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A novel feature selection algorithm using decomposition based multi-objective guided honey badger algorithm (MO-GHBA) and NSGA-III
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Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer's disease progression
TL;DR: In this article , a Siamese neural network is used to detect the asymmetry between the left and right brain hemispheres during progressive brain degeneration, from mild cognitive impairment to severe atrophy associated with Alzheimer's disease.
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Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
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Combining Pattern Classifiers: Methods and Algorithms
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