Add to ORKGMany machine/deep-learning models have been introduced to perform data classification. • An open question in the research for data classification is the presence of a class imbalance problem. • The aim of this project is to design a deep neural network with an incremental learning capability to classify imbalanced data
This thesis focuses on the study of machine learning and pattern recognition algorithms for imbalanc...
Imbalance datasets exist in many real-world domains. It is straightforward to apply classification a...
In this project, the Ensemble Deep Random Vector Functional Link (edRVFL) network has been modified ...
Abstract The purpose of this study is to examine existing deep learning techniques for addressing cl...
Abstract — In this paper, we propose an extended deep learning approach that incorporates instance s...
Imbalanced data is a major problem in machine learning classification, since predictive performance ...
Imbalanced class is one of the trials in classifying materials of big data. Data disparity produces ...
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfacto...
Imbalanced class is one of the challenges in classifying big data. Data disparity produces a biased ...
Deep neural networks generally perform poorly with datasets that suffer from quantity imbalance and ...
Most existing classification approaches assume the underlying training set is evenly distributed. In...
Abstract. Incremental on-line learning is a research topic gaining increasing interest in the machin...
Imbalanced class is one of the trials in classifying materials of big data. Data disparity produces ...
The first book of its kind to review the current status and future direction of the exciting new bra...
The imbalance classification is a common problem in the field of data mining.In general,the skewed d...
This thesis focuses on the study of machine learning and pattern recognition algorithms for imbalanc...
Imbalance datasets exist in many real-world domains. It is straightforward to apply classification a...
In this project, the Ensemble Deep Random Vector Functional Link (edRVFL) network has been modified ...
Abstract The purpose of this study is to examine existing deep learning techniques for addressing cl...
Abstract — In this paper, we propose an extended deep learning approach that incorporates instance s...
Imbalanced data is a major problem in machine learning classification, since predictive performance ...
Imbalanced class is one of the trials in classifying materials of big data. Data disparity produces ...
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfacto...
Imbalanced class is one of the challenges in classifying big data. Data disparity produces a biased ...
Deep neural networks generally perform poorly with datasets that suffer from quantity imbalance and ...
Most existing classification approaches assume the underlying training set is evenly distributed. In...
Abstract. Incremental on-line learning is a research topic gaining increasing interest in the machin...
Imbalanced class is one of the trials in classifying materials of big data. Data disparity produces ...
The first book of its kind to review the current status and future direction of the exciting new bra...
The imbalance classification is a common problem in the field of data mining.In general,the skewed d...
This thesis focuses on the study of machine learning and pattern recognition algorithms for imbalanc...
Imbalance datasets exist in many real-world domains. It is straightforward to apply classification a...
In this project, the Ensemble Deep Random Vector Functional Link (edRVFL) network has been modified ...