On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey
Abstract Introduction The number of Alzheimer's Disease (AD) patients is increasing with
increased life expectancy and 115.4 million people are expected to be affected in 2050 …
increased life expectancy and 115.4 million people are expected to be affected in 2050 …
Class imbalance ensemble learning based on the margin theory
The proportion of instances belonging to each class in a data-set plays an important role in
machine learning. However, the real world data often suffer from class imbalance. Dealing …
machine learning. However, the real world data often suffer from class imbalance. Dealing …
New margin-based subsampling iterative technique in modified random forests for classification
Diversity within base classifiers has been recognized as an important characteristic of an
ensemble classifier. Data and feature sampling are two popular methods of increasing such …
ensemble classifier. Data and feature sampling are two popular methods of increasing such …
Semi-supervised rotation forest based on ensemble margin theory for the classification of hyperspectral image with limited training data
In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for
the classification of hyperspectral images with limited training data. Our proposition is based …
the classification of hyperspectral images with limited training data. Our proposition is based …
Dynamic synthetic minority over-sampling technique-based rotation forest for the classification of imbalanced hyperspectral data
Rotation forest (RoF) is a powerful ensemble classifier and has attracted substantial
attention due to its performance in hyperspectral data classification. Multi-class imbalance …
attention due to its performance in hyperspectral data classification. Multi-class imbalance …
A label noise filtering method for regression based on adaptive threshold and noise score
C Li, Z Mao - Expert Systems with Applications, 2023 - Elsevier
The quality of training data plays a decisive role in the establishment of intelligent models.
Since raw data obtained from the real world are usually entwined with noise due to variety of …
Since raw data obtained from the real world are usually entwined with noise due to variety of …
CNC-NOS: Class noise cleaning by ensemble filtering and noise scoring
Obtaining data in the real world is subject to imperfections and the appearance of noise is a
common consequence of such flaws. In classification, class noise will deteriorate the …
common consequence of such flaws. In classification, class noise will deteriorate the …
Noise correction to improve data and model quality for crowdsourcing
In supervised learning, obtaining expert labeling of data is expensive and time-consuming in
many cases. Crowdsourcing services provide a cheap and efficient way to acquire the labels …
many cases. Crowdsourcing services provide a cheap and efficient way to acquire the labels …
Three-teaching: A three-way decision framework to handle noisy labels
G Chao, K Zhang, X Wang, D Chu - Applied Soft Computing, 2024 - Elsevier
Learning with noisy labels represents a prevalent weakly supervised learning paradigm.
Uncertain knowledge resulting from noisy labels poses significant challenges for knowledge …
Uncertain knowledge resulting from noisy labels poses significant challenges for knowledge …
[HTML][HTML] Multiple instance classification: Bag noise filtering for negative instance noise cleaning
Data in the real world is far from being perfect. The appearance of noise is a common issue
that arises from the limitations of data acquisition mechanisms and human knowledge. In …
that arises from the limitations of data acquisition mechanisms and human knowledge. In …