[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
[HTML][HTML] A comprehensive data level analysis for cancer diagnosis on imbalanced data
The early diagnosis of cancer, as one of the major causes of death, is vital for cancerous
patients. Diagnosing diseases in general and cancer in particular is a considerable …
patients. Diagnosing diseases in general and cancer in particular is a considerable …
End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
A survey on long-tailed visual recognition
The heavy reliance on data is one of the major reasons that currently limit the development
of deep learning. Data quality directly dominates the effect of deep learning models, and the …
of deep learning. Data quality directly dominates the effect of deep learning models, and the …
On the class overlap problem in imbalanced data classification
Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …
existing and recent literature showed that class overlap had a higher negative impact on the …
Neighbourhood-based undersampling approach for handling imbalanced and overlapped data
Class imbalanced datasets are common across different domains including health, security,
banking and others. A typical supervised learning algorithm tends to be biased towards the …
banking and others. A typical supervised learning algorithm tends to be biased towards the …
Towards DNA based data security in the cloud computing environment
Nowadays, data size is increasing day by day from gigabytes to terabytes or even petabytes,
mainly because of the evolution of a large amount of real-time data. Most of the big data is …
mainly because of the evolution of a large amount of real-time data. Most of the big data is …
Class-overlap undersampling based on Schur decomposition for Class-imbalance problems
The class-imbalance problem is an important area that plagues machine learning and data
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …
Handling data irregularities in classification: Foundations, trends, and future challenges
Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …
of similar underlying class distributions, balanced size of classes, the presence of a full set of …
Data sampling methods to deal with the big data multi-class imbalance problem
The class imbalance problem has been a hot topic in the machine learning community in
recent years. Nowadays, in the time of big data and deep learning, this problem remains in …
recent years. Nowadays, in the time of big data and deep learning, this problem remains in …