A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
Conventional to deep ensemble methods for hyperspectral image classification: A comprehensive survey
Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
Classification of Maize leaf diseases from healthy leaves using Deep Forest
J Arora, U Agrawal - Journal of Artificial Intelligence and Systems, 2020 - iecscience.org
Apart from being relied upon for feeding the entire world, the agricultural sector is also
responsible for a third of the global Gross-Domestic-Product (GDP). Additionally, a majority …
responsible for a third of the global Gross-Domestic-Product (GDP). Additionally, a majority …
Hyperspectral image classification based on convolutional neural network and random forest
Deep learning-based methods, especially deep convolutional neural network (CNN), have
proven their powerfulness in hyperspectral image (HSI) classification. On the other hand …
proven their powerfulness in hyperspectral image (HSI) classification. On the other hand …
Hyperspectral imaging technology combined with deep forest model to identify frost-damaged rice seeds
L Zhang, H Sun, Z Rao, H Ji - Spectrochimica acta part A: molecular and …, 2020 - Elsevier
In recent years, deep learning models have been widely used in the field of hyperspectral
imaging. However, the training of deep learning models requires not only a large number of …
imaging. However, the training of deep learning models requires not only a large number of …
Multi-feature input deep forest for EEG-based emotion recognition
Due to the rapid development of human–computer interaction, affective computing has
attracted more and more attention in recent years. In emotion recognition …
attracted more and more attention in recent years. In emotion recognition …
Epileptic seizure detection based on variational mode decomposition and deep forest using EEG signals
Electroencephalography (EEG) records the electrical activity of the brain, which is an
important tool for the automatic detection of epileptic seizures. It is certainly a very heavy …
important tool for the automatic detection of epileptic seizures. It is certainly a very heavy …
DF classification algorithm for constructing a small sample size of data-oriented DF regression model
The deep forest (DF) model is built using a multilayer ensemble of forest units through
decision tree aggregation. DF presents characteristics of an easy-to-understand structure, is …
decision tree aggregation. DF presents characteristics of an easy-to-understand structure, is …
GPU-accelerated CatBoost-forest for hyperspectral image classification via parallelized mRMR ensemble subspace feature selection
In this article, the graphics processing unit (GPU)-accelerated CatBoost (GPU-CatBoost)
algorithm for hyperspectral image classification is first introduced and comparatively studied …
algorithm for hyperspectral image classification is first introduced and comparatively studied …
A hybrid classification of imbalanced hyperspectral images using ADASYN and enhanced deep subsampled multi-grained cascaded forest
Hyperspectral image (HSI) analysis generally suffers from issues such as high
dimensionality, imbalanced sample sets for different classes, and the choice of classifiers for …
dimensionality, imbalanced sample sets for different classes, and the choice of classifiers for …