Text classification based on ensemble extreme learning machine
This work addresses text classification, particularly for balanced and imbalanced multiclass scenarios, but appears incremental as it builds on existing ELM and AdaBoost methods.
The authors tackled the problem of text classification by proposing a cost-sensitive ensemble weighted extreme learning machine (AE1-WELM) that addresses sample differences within and between categories, resulting in an accurate, reliable, and effective solution as shown in experimental results.
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.