The Extreme Learning Machine(ELM) and its variants are effective in many machine learning applications such as Imbalanced Learning(IL) or Big Data(BD) learning. However, they are unable to solve both imbalanced and large-volume data learning problems. This study addresses the IL problem in BD applications. The Distributed and Weighted ELM(DW-ELM) algorithm is proposed, which is based on the Map Reduce framework. To confirm the feasibility of parallel computation, first, the fact that matrix multiplication operators are decomposable is illustrated.Then, to further improve the computational efficiency, an Improved DW-ELM algorithm(IDW-ELM) is developed using only one Map Reduce job. The successful operations of the proposed DW-ELM and IDW-ELM algorithms are finally validated through experiments. The Extreme Learning Machine (ELM) and its variants are effective in many machine learning applications such as Imbalanced Learning (IL) or Big Data (BD) learning. However, they are unable to solve both imbalanced and large-volume data learning problems. This The distributed and Weighted ELM (DW-ELM) algorithm is proposed, which is based on the Map Reduce framework. To confirm the feasibility of parallel computation, first, the fact that matrix multiplication operators are decomposable is illustrated. to further improve the computational efficiency, an improved DW-ELM algorithm (IDW-ELM) is developed using only one Map Reduce job. The successful operations of the proposed DW-ELM and IDW-ELM algorithms are finally validated through experiments.