Enhanced Kernel-Based Multilayer Fuzzy Weighted Extreme Learning Machines

Abstract

The high-dimensional and imbalanced data classification appears in many actual applications, but there are many problems encountered in practical operation. To overcome the disadvantages of kernel-based multilayer extreme learning machines (ML-KELM), enhanced kernel-based multilayer fuzzy weighted extreme learning machines (EML-KFWELM) has been proposed in this study. First, ML-KELM ignores imbalance learning, so we embed weighted strategy into ML-KELM to enhance the classification performance of the minority class. Meanwhile, we propose fuzzy membership to eliminate classification error caused by outlier and noise samples. Furthermore, we develop an enhanced grey wolf optimization (EGWO) method to perform the parameters optimization and improve the generalization performance of ML-KELM. In addition, the advantage of EML-KFWELM is that representation learning and classification can be integrated into a single learning process. Finally, computational comparisons with other state-of-the-art methods are performed on various real-world and gene expression data. Experimental results demonstrate that the proposed EML-KFWELM has good stability and can efficiently deal with the high-dimensional and imbalanced data.

Keywords

• Imbalanced data
• kernel-based multilayer extreme learning machines
• fuzzy membership
• grey wolf optimization