Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Learning concepts from large scale imbalanced data sets using support cluster machines
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Letters: Convex incremental extreme learning machine
Neurocomputing
Deterministic neural classification
Neural Computation
IEEE Transactions on Knowledge and Data Engineering
Face recognition based on kernelized extreme learning machine
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Kernel-Based Two-Class Classifier for Imbalanced Data Sets
IEEE Transactions on Neural Networks
Boosting weighted ELM for imbalanced learning
Neurocomputing
Hi-index | 0.01 |
Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are ''generalized'' single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution, a weighted ELM is proposed which is able to generalize to balanced data. The proposed method maintains the advantages from original ELM: (1) it is simple in theory and convenient in implementation; (2) a wide type of feature mapping functions or kernels are available for the proposed framework; (3) the proposed method can be applied directly into multiclass classification tasks. In addition, after integrating with the weighting scheme, (1) the weighted ELM is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM; (2) by assigning different weights for each example according to users' needs, the weighted ELM can be generalized to cost sensitive learning.