scholarly journals Mexican Hat Wavelet Kernel ELM for Multiclass Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Jie Wang ◽  
Yi-Fan Song ◽  
Tian-Lei Ma
Keyword(s):  
Multiclass Classification ◽  
Test Accuracy ◽  
Data Sets ◽  
Classification Problems ◽  
Training Time ◽  
Existing Problems ◽  
Mexican Hat Wavelet ◽  
Mexican Hat ◽  
Learning Machine ◽  
Multiclass Classification Problems

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers.

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.

On Binary Reduction of Large-Scale Multiclass Classification Problems

2015 ◽  
pp. 132-144 ◽  
Author(s):  
Bikash Joshi ◽  
Massih-Reza Amini ◽  
Ioannis Partalas ◽  
Liva Ralaivola ◽  
Nicolas Usunier ◽  
...  
Keyword(s):  
Large Scale ◽  
Multiclass Classification ◽  
Classification Problems ◽  
Multiclass Classification Problems

scholarly journals A variable selection method for multiclass classification problems using two-class ROC analysis

2018 ◽  
Vol 177 ◽  
pp. 35-46 ◽  
Author(s):  
Miguel de Figueiredo ◽  
Christophe B.Y. Cordella ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
Xavier Archer ◽  
Jean-Marc Bégué ◽  
...  
Keyword(s):  
Variable Selection ◽  
Roc Analysis ◽  
Multiclass Classification ◽  
Selection Method ◽  
Classification Problems ◽  
Variable Selection Method ◽  
Multiclass Classification Problems

THE DIMENSION OF ECOCs FOR MULTICLASS CLASSIFICATION PROBLEMS

2008 ◽  
Vol 17 (03) ◽  
pp. 433-447 ◽  
Author(s):  
EDGAR PIMENTA ◽  
JOÃO GAMA ◽  
ANDRÉ CARVALHO
Keyword(s):  
Error Correction ◽  
Decomposition Methods ◽  
Multiclass Classification ◽  
Classification Problems ◽  
Trade Off ◽  
Different Dimensions ◽  
Good Trade ◽  
Number Of Classes ◽  
Binary Problems ◽  
Multiclass Classification Problems

Several classification problems involve more than two classes. These problems are known as multiclass classification problems. One of the approaches to deal with multiclass problems is their decomposition into a set of binary problems. Recent work shows important advantages related with this approach. Several strategies have been proposed for this decomposition. The strategies most frequently used are All-vs-All, One-vs-All and Error Correction Output Codes (ECOC). ECOCs are based on binary words (codewords) and have been adapted to deal with multiclass problems. For such, they must comply with a number of specific constraints. Different dimensions may be adopted for the codewords for each number of classes in the problem. These dimensions grow exponentially with the number of classes present in a dataset. Two methods to choose the dimension of a ECOC, which assure a good trade-off between redundancy and error correction capacity, are proposed in this paper. The proposed methods are evaluated in a set of benchmark classification problems. Experimental results show that they are competitive with other multiclass decomposition methods.

scholarly journals Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Liu ◽  
Bo He ◽  
Diya Dong ◽  
Yue Shen ◽  
Tianhong Yan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Online Learning ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Data Sets ◽  
Classification Problems ◽  
Swarm Optimization ◽  
Fast Learning ◽  
Selective Ensemble ◽  
Learning Machine

A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, including batch learning and online learning. Second, an adaptive selective ensemble framework for online learning is designed to balance the accuracy and speed of the algorithm. Experiments for both regression and classification problems with UCI data sets are carried out. Comparisons between OS-ELM, simple ensemble OS-ELM (EOS-ELM), genetic algorithm based selective ensemble (GASEN) of OS-ELM, and the proposed particle swarm optimization based selective ensemble of OS-ELM empirically show that the proposed algorithm achieves good generalization performance and fast learning speed.

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