Text Classification Method Based on Multi-layer Extreme Learning Machine

When the dimension of text data is high, the regularized extreme learning machine (ELM) of single hidden layer structure has not enough ability to express feature in the text classification. To solve the problem, this paper presented a text classification method based on multi-layer extreme learning machine (ML-ELM). First, the method used the compressed representation of extreme learning machine-based auto-encoder (ELM-AE) to reduce the dimension of the text data. Then, the structure of the multi-hidden was used to represent high-level features in the text data, and the method of least squares was used to classify the text data. The experimental results on Reuters, 20newsgroup and Fudan University Chinese Corpus datasets show that this algorithm has a good classification performance compared with other algorithms.