… objective directly reduces the multi-class classification error of the decision tree. … extreme
multi-class classification and explore the connection of this objective to entropy-based criteria. …

… We consider the multi class classification problem under the … a simple top-down criterion for
purification of labels that allows … objective function (splitting criterion) leads simultaneously to …

… classification. Different methods have been proposed in order for them to be applied in
multiclass classification … in the implementation of SVM in multiclass classification applications [8]. …

… Extreme Learning Machine (ELM) is adopted to handle both binary and multiclass classification
… Second, the hidden layer is encoded by an extremely small approximate empirical …

… not suitable for the imbalanced multiclass classification. Overall, the … multiclass imbalanced
distribution and stream-based data are insufficient. In addition, when dealing with multiclass …

… In this scheme, considering the multiple classification performance criteria, a multi-objective …
we use 10 binary classification datasets and 2 multiclass classification datasets, which have …

… We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel …
norm of output weights criteria. The proposed formulation also addresses aspects of quaternary …

… samples of minority class are extremely rare (saying we just … find criteria to select the best
approach for each type of data. … ensemble learning algorithm for multi-class imbalanced data, …

… and efficient one-class classifier based on extreme learning machine (ELM). The essence of
… ELM, which provides a unified solution for regression, binary, and multiclass classifications. …

… Extreme Learning Machines (ELMs). The main goal is to provide probabilities as outputs
for Multiclass Classification … In the proposed methodology, we are using the BIC criterion to …