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Voting Based Extreme Learning Machine Essay

1422 words - 6 pages

Real valued classification is a popular decision making problem, having wide
practical application in various fields. Extreme Learning Machine (ELM) pro-
posed by Huang et al.[1], is an effective machine learning technique for real val-
ued classification. ELMis a single hidden layer feedfo5 rward network in which the
weights between input and hidden layer are initialized randomly. ELM uses ana-
lytical approach to compute weights between hidden and output layer [2], which
makes it faster compared to other gradient based classifiers ([3, 4]). Various
variants of ELM were recently proposed, which includes Incremental Extreme
10 Learning Machine [5], Kernelized Extreme Learning Machine [6], ...view middle of the document...

are some of the complex-
valued classifiers designed for real valued classification problems. CCELM out-
performs other complex valued classifiers for real valued classification problems.
It also performs well when dataset is imbalanced.
35 It has been observed that many practical classification problems have im-
balanced data sets[23, 24]. If we classify such data, most of the classifiers favours
the majority class due to which most of the instances belonging to minority class
are misclassified. To deal with such dataset, various sampling approaches [25]
as well as algorithmic approaches are used. Sampling approaches includes over-
40 sampling and undersampling techniques. Oversampling replicates a fraction of
minority samples while undersampling approach reduces a fraction of major-
ity samples to make dataset balanced. But there is problem with sampling
approaches. Oversampling [26] increases redundancy of data and undersam-
pling results in loss of information. In algorithmic approach, classifier design
45 encompasses the measures to handle class imbalance. Most of the neural net-
work based classifiers like FCRBF [4, 3],CCELM[9] minimizes least square error
to find optimal weights. Recently proposed WELM minimizes weighted least
square error function to find optimal weights between hidden and output layer.
In this classifier, residuals of minority class are assigned more weights compared
50 to majority class. This increases the impact of minority samples. Finding opti-
mal weighting scheme is a challenging task. WELM [7] proposes and evaluates
two generalized weight schemes for assigning weights to the instances.

Works Cited

[1] G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: theory
300 and applications, Neurocomputing 70 (1-3) (2006) 489–501.
2] M.-B. Li, G.-B. Huang, P. Saratchandran, N. Sundararajan, Fully complex
extreme learning machine, Neurocomputing 68 (1-4) (2005) 306–314.
[3] R. Savitha, S. Suresh, N. Sundararajan, H. J. Kim, A fully complex-valued
radial basis function classifier for real valued classification problems, Neu-
rocomputin305 g 78 (1) (2012) 104–110.
[4] R. Savitha, S. Suresh, N. Sundararajan, A fully complex valued radial
basis function network and its learning algorithm, International Journal of
Neural Systems 19 (4) (2009) 253–267.
[5] G.-B. Huang, M.-B. Li, L. Chen, C. K. Siew, Incremental extreme learning
310 machine with fully complex hidden nodes, Neurocomputing 71 (4-6) (2008)
576–583.
[6] G.-B. Huang, H. Zhou, X. Ding, R. Zhang, Extreme learning machine for
regression and multiclass classification, IEEE Trans. on Syst. Man and
Cybern., Part B 42 (2) (2012) 513–529.
315 [7] W. Zong, G.-B. Huang, Y. Chen, Weighted extreme learning machine for
imbalance learning, Neurocomputing 101 (2013) 229–242.
[8] W. Deng, Q. Zheng, L. Chen, Regularized extreme learning machine, in:
IEEE Symposium on Computational Intelligence and Data Mining (CIDM
09), March 30 - April 2, 2009, pp. 389–395.
320 [9] R....

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