Lu Huijuan, Wei Shasha, Zhou Zili, Miao Yanzi, Lu Yi
Int J Data Min Bioinform. 2015;12(3):294-312. doi: 10.1504/ijdmb.2015.069657.
The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.
传统分类算法在生物信息学应用中的主要目的是获得更高的分类准确率。然而，这些算法无法满足将平均误分类成本降至最低的要求。本文提出了一种新的成本敏感正则化极限学习机（CS-RELM）算法，通过概率估计和误分类成本来重构分类结果。通过提高一组误分类成本较高的小样本的分类准确率，新的CS-RELM可以将分类成本降至最低。“拒绝成本”被集成到CS-RELM算法中，以进一步降低平均误分类成本。通过使用结肠癌数据集和SRBCT（小圆蓝细胞肿瘤）数据集，将CS-RELM与其他成本敏感算法进行了比较，如极限学习机（ELM）、成本敏感极限学习机、正则化极限学习机、成本敏感支持向量机（SVM）。实验结果表明，嵌入拒绝成本的CS-RELM可以降低平均误分类成本，并且比其他算法做出更可靠的分类决策。
