CAJ | 학술논문

针对极端学习机算法对不平衡数据分类问题的处理效果不够理想，提出了一种基于聚类欠采样的极端学习机算法。新算法首先对训练集的负类样本进行聚类生成不同的簇，然后在各簇中按规定的采样率对其进行欠采样，取出的样本组成新的负类数据集，从而使训练集正负类数据个数达到相对平衡，最后训练分类器对测试集进行测试。实验结果表明，新算法有效地降低了数据的不平衡对分类准确率的影响，具有更好的分类性能。
침대겁단학습궤산법대불평형수거분류문제적처리효과불구이상，제출료일충기우취류흠채양적겁단학습궤산법。신산법수선대훈련집적부류양본진행취류생성불동적족，연후재각족중안규정적채양솔대기진행흠채양，취출적양본조성신적부류수거집，종이사훈련집정부류수거개수체도상대평형，최후훈련분류기대측시집진행측시。실험결과표명，신산법유효지강저료수거적불평형대분류준학솔적영향，구유경호적분류성능。
Aiming at the problem that Extreme Learning Machine(ELM) is unsatisfying in dealing with imbalanced data set, an ELM algorithm based on clustering and under-sampling is proposed. Firstly, the new algorithm clusters the negative samples of training set and generates different clusters. Secondly, it takes samples in every cluster according the specified sampling rate, the data sampled make up a new negative data set, which can make the positive and negative data balanced in training set. Lastly, it trains the classifier and predicts the test set. The experimental results show that the new algorithm can effectively reduce the influence of imbalanced data for classification accuracy and has better classification performance.