Search Results for author:
Found 9 papers, 2 papers with code
Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning
To overcome this limitation, we propose a novel graph embedded intuitionistic fuzzy RVFL for class imbalance learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets.
Heterogeneous Oblique Double Random Forest
Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node.
Deep Learning for Brain Age Estimation: A Systematic Review
In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data.
Diagnosis of Schizophrenia: A comprehensive evaluation
Our evaluation showed that classification algorithms along with the feature selection approaches impact the diagnosis of Schizophrenia disease.
Random vector functional link network: recent developments, applications, and future directions
To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed.
Oblique and rotation double random forest
The oblique double random forest models are multivariate decision trees.
Comprehensive Review On Twin Support Vector Machines
Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively.
Ensemble deep learning: A review
Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance.
Identification of Chimera using Machine Learning
The oblique random forest with null space regularization achieved consistent performance (more than $83\%$ accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance.
