Learning ELM-Tree from big data based on uncertainty reduction
A challenge in big data classification is the design of highly parallelized learning algorithms. One solution to this problem is applying parallel computation to different components of a learning model. In this paper, we first propose an extreme learning machine tree (ELM-Tree) model based on the heuristics of uncertainty reduction. In the ELM-Tree model, information entropy and ambiguity are used as the uncertainty measures for splitting decision tree (DT) nodes. Besides, in order to resolve the over-partitioning problem in the DT induction, ELMs are embedded as the leaf nodes when the gain ratios of all the available splits are smaller than a given threshold. Then, we apply parallel computation to five components of the ELM-Tree model, which effectively reduces the computational time for big data classification. Experimental studies demonstrate the effectiveness of the proposed method.
elm-tree uncertainty reduction big data classification extreme learning machine decision tree