A hierarchical structure of extreme learning machine (HELM) for high-dimensional datasets with noise

Extreme Learning Machine (ELM), a competitive machine learning technique for single-hidden-layer feedforward neural networks (SLFNNs), is simple in theory and fast in implementation. To deal with high-dimensional data with noise, ELM with a hierarchical structure (HELM) is proposed in this paper. The proposed HELM consists of two parts: some groups of subnets and a main net. The subnets are based on some well-trained auto-associative neural networks (AANNs), which can reduce dimension and filter out noise. The main net is based on the traditional ELM. Additionally, from the perspective of data attributes spaces (DASs), the difficulties in designing subnets are avoided by using a method of Data Attributes Extension Classification (DAEC). Experiments on five high-dimensional datasets with noise are carried out to examine the HELM model. Experimental results show that HELM has higher accuracy with fewer neurons in the main net than ELM.