Performance enhancement of extreme learning machine for multi-category sparse data classification problems

2010 ◽  
Vol 23 (7) ◽  
pp. 1149-1157 ◽  
Author(s):  
S. Suresh ◽  
S. Saraswathi ◽  
N. Sundararajan
Keyword(s):  
Extreme Learning Machine ◽  
Performance Enhancement ◽  
Data Classification ◽  
Sparse Data ◽  
Classification Problems ◽  
Learning Machine

Cancer data classification using binary bat optimization and extreme learning machine with a novel fitness function

2019 ◽  
Vol 57 (12) ◽  
pp. 2673-2682 ◽  
Author(s):  
Kaveri Chatra ◽  
Venkatanareshbabu Kuppili ◽  
Damodar Reddy Edla ◽  
Ajeet Kumar Verma
Keyword(s):  
Extreme Learning Machine ◽  
Fitness Function ◽  
Data Classification ◽  
Cancer Data ◽  
Learning Machine

scholarly journals A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1284
Author(s):  
Licheng Cui ◽  
Huawei Zhai ◽  
Hongfei Lin
Keyword(s):  
Least Squares ◽  
Extreme Learning Machine ◽  
Weight Matrix ◽  
Testing Time ◽  
Least Squares Regression ◽  
Classification Problems ◽  
Training Time ◽  
Discrimination Ability ◽  
Learning Machine ◽  
Hidden Layer

An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELM has better performance in training time, testing time and accuracy than ELM and OELM.

scholarly journals Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang-Rui Ren ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Junliang Shang ◽  
Chun-Hou Zheng
Keyword(s):  
Extreme Learning Machine ◽  
High Performance ◽  
Structural Information ◽  
Optimization Method ◽  
Classification Performance ◽  
Weight Matrix ◽  
New Method ◽  
Classification Problems ◽  
Graph Regularization ◽  
Learning Machine

Abstract Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

Elastic extreme learning machine for big data classification

2015 ◽  
Vol 149 ◽  
pp. 464-471 ◽  
Author(s):  
Junchang Xin ◽  
Zhiqiong Wang ◽  
Luxuan Qu ◽  
Guoren Wang
Keyword(s):  
Big Data ◽  
Extreme Learning Machine ◽  
Data Classification ◽  
Big Data Classification ◽  
Learning Machine
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