Classification with Extreme Learning Machine and Ensemble Algorithms Over Randomly Partitioned Data
This work addresses the problem of automated classification for big data applications, but it appears incremental as it combines existing methods like ELM and AdaBoosting in a distributed framework.
The paper tackles the challenge of building predictive models from complex, large-scale datasets by exploring a MapReduce-based Distributed AdaBoosting approach with Extreme Learning Machines (ELM) to create ensemble classification models, applied to publicly available data mining datasets.
In this age of Big Data, machine learning based data mining methods are extensively used to inspect large scale data sets. Deriving applicable predictive modeling from these type of data sets is a challenging obstacle because of their high complexity. Opportunity with high data availability levels, automated classification of data sets has become a critical and complicated function. In this paper, the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) are explored to build reliable predictive bag of classification models. Thus, (i) dataset ensembles are build; (ii) ELM algorithm is used to build weak classification models; and (iii) build a strong classification model from a set of weak classification models. This training model is applied to the publicly available knowledge discovery and data mining datasets.