Efficient representation learning for high-dimensional imbalance data
Bilal Mirza, Stanley Kok, Zhiping Lin, Yong Kiang Yeo, Xiaoping Lai, Jiuwen Cao, José Sepúlveda · 2016
In this paper, a multi-layer weighted extreme learning machine (ML-WELM) is proposed for high-dimensional datasets with class imbalance. The recently proposed single hidden layer WELM method effectively tackles class imbalance but it may not capture high level abstractions in image datasets. ML-WELM provides efficient representation learning for big image data using multiple hidden layers and at the same time tackles the class imbalance problem using cost-sensitive weighting. Weighted ELM auto-encoder (WELM-AE) is also proposed for layer-by-layer class imbalance feature learning in ML-WELM. We used four imbalance image datasets in our experiments; ML-WELM performs better than the WELM method on all of them.