  In this paper, we propose a novel approach based on cost-sensitive ensemble
weighted extreme learning machine; we call this approach AE1-WELM. We apply
this approach to text classification. AE1-WELM is an algorithm including
balanced and imbalanced multiclassification for text classification. Weighted
ELM assigning the different weights to the different samples improves the
classification accuracy to a certain extent, but weighted ELM considers the
differences between samples in the different categories only and ignores the
differences between samples within the same categories. We measure the
importance of the documents by the sample information entropy, and generate
cost-sensitive matrix and factor based on the document importance, then embed
the cost-sensitive weighted ELM into the AdaBoost.M1 framework seamlessly.
Vector space model(VSM) text representation produces the high dimensions and
sparse features which increase the burden of ELM. To overcome this problem, we
develop a text classification framework combining the word vector and AE1-WELM.
The experimental results show that our method provides an accurate, reliable
and effective solution for text classification.
