关键词: 极限学习机, 不确定, 抽样, 分类

Abstract: Large amounts of data in real-world applications have inherent uncertainty. Traditional learning algorithms cannot be applied directly onto uncertain datasets. Aiming at classification problems over uncertain data， a sampling based uncertain ELM(extreme learning machine) was proposed. Instances were first sampled out of uncertain objects， and then learnt with uncertain ELM. The uncertain objects would be assigned to their classes respectively according to the probabilities aggregation method. The classification was realized by the proposed algorithm in this paper over uncertain data avoiding the enumeration of instances. The experimental results indicated the efficiency and effectiveness of our algorithm.

Key words: ELM(extreme learning machine), uncertain, sampling, classification