研究目的
Investigating the performance of a regularized weighted circular complex-valued extreme learning machine (RWCC-ELM) for imbalanced learning.
研究成果
RWCC-ELM outperforms CC-ELM and WELM for most of the evaluated datasets, demonstrating its effectiveness in handling imbalanced learning problems. The superiority of RWCC-ELM is also confirmed by statistical tests. Future work may include applying RWCC-ELM to real-world applications with complex-valued inputs.
研究不足
The study is limited to imbalanced datasets from the Keel repository and does not explore the application of RWCC-ELM on real-world complex-valued input data with large class distribution variations.
1：Experimental Design and Method Selection:
The study involves the design of RWCC-ELM, incorporating the strengths of CC-ELM and WELM, and evaluates its performance on imbalanced datasets.
2：Sample Selection and Data Sources:
Imbalanced datasets from the Keel repository are used for evaluation.
3：List of Experimental Equipment and Materials:
The experiments are conducted using Matlab
4：1 on a PC with Intel core i5 processor, 20 GHz CPU and 2 GB RAM. Experimental Procedures and Operational Workflow:
The proposed RWCC-ELM is compared with CC-ELM and WELM using various performance metrics.
5：Data Analysis Methods:
Performance is evaluated using G-mean and overall accuracy, with statistical significance tested using the Wilcoxon signed-rank test.
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