An insight into imbalanced big data classification: outcomes and challenges
Big Data applications are emerging during the last years, and researchers from many
disciplines are aware of the high advantages related to the knowledge extraction from this …
disciplines are aware of the high advantages related to the knowledge extraction from this …
SMOTE for handling imbalanced data problem: A review
Imbalanced class data distribution occurs when the number of examples representing one
class is much lower than others. This conditioning affects the prediction accuracy degraded …
class is much lower than others. This conditioning affects the prediction accuracy degraded …
Combining convolutional neural network with recursive neural network for blood cell image classification
G Liang, H Hong, W Xie, L Zheng - IEEE access, 2018 - ieeexplore.ieee.org
The diagnosis of blood-related diseases involves the identification and characterization of a
patient's blood sample. As such, automated methods for detecting and classifying the types …
patient's blood sample. As such, automated methods for detecting and classifying the types …
Machine learning techniques for liquid level estimation using FBG temperature sensor array
KP Nascimento, A Frizera-Neto, C Marques… - Optical Fiber …, 2021 - Elsevier
This paper proposes the use of the fiber Bragg grating (FBG) temperature sensors array to
estimate the fluid level. The tank is 100 cm in height and 30 cm in width, with 9 FBG sensors …
estimate the fluid level. The tank is 100 cm in height and 30 cm in width, with 9 FBG sensors …
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
In many healthcare applications, datasets for classification may be highly imbalanced due to
the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority …
the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority …
A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization
Dynamic multi-objective optimization problems (DMOPs) are multi-objective optimization
problems in which at least one objective and/or related parameter vary over time. The …
problems in which at least one objective and/or related parameter vary over time. The …
A binary PSO-based ensemble under-sampling model for rebalancing imbalanced training data
Ensemble technique and under-sampling technique are both effective tools used for
imbalanced dataset classification problems. In this paper, a novel ensemble method …
imbalanced dataset classification problems. In this paper, a novel ensemble method …
Adaptive multi-objective swarm fusion for imbalanced data classification
Learning a classifier from an imbalanced dataset is an important problem in data mining and
machine learning. Since there is more information from the majority classes than the …
machine learning. Since there is more information from the majority classes than the …
Adaptive swarm balancing algorithms for rare-event prediction in imbalanced healthcare data
Clinical data analysis and forecasting have made substantial contributions to disease
control, prevention and detection. However, such data usually suffer from highly imbalanced …
control, prevention and detection. However, such data usually suffer from highly imbalanced …
Machine-learning-based olfactometer: prediction of odor perception from physicochemical features of odorant molecules
Gas chromatography/olfactometry (GC/O) has been used in various fields as a valuable
method to identify odor-active components from a complex mixture. Since human assessors …
method to identify odor-active components from a complex mixture. Since human assessors …