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., 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 , 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 , Kernelized Extreme Learning Machine , ...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 
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  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 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  proposes and evaluates
two generalized weight schemes for assigning weights to the instances.
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320  R....