Abstract
Cost-sensitive learning is a crucial problem in machine learning research. Traditional classification problem assumes that the misclassification for each category has the same cost, and the target of learning algorithm is to minimize the expected error rate. In cost-sensitive learning, costs of misclassification for samples of different categories are not the same; the target of algorithm is to minimize the sum of misclassification cost. Cost-sensitive learning can meet the actual demand of real-life classification problems, such as medical diagnosis, financial projections, and so on. Due to fast learning speed and perfect performance, extreme learning machine (ELM) has become one of the best classification algorithms, while voting based on extreme learning machine (V-ELM) makes classification results more accurate and stable. However, V-ELM and some other versions of ELM are all based on the assumption that all misclassifications have same cost. Therefore, they cannot solve cost-sensitive problems well. To overcome the drawback of ELMs mentioned above, an algorithm called cost-sensitive ELM (CS-ELM) is proposed by introducing misclassification cost of each sample into V-ELM. Experimental results on gene expression data show that CS-ELM is effective in reducing misclassification cost.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (no. 61272315, no. 60842009, and no. 60905034), Zhejiang Provincial Natural Science Foundation (no. Y1110342, no. Y1080950) and the Pao Yu-Kong and Pao Zhao-Long Scholarship for Chinese Students Studying Abroad.
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Lu, Hj., Zheng, Eh., Lu, Y. et al. ELM-based gene expression classification with misclassification cost. Neural Comput & Applic 25, 525–531 (2014). https://doi.org/10.1007/s00521-013-1512-x
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DOI: https://doi.org/10.1007/s00521-013-1512-x