Classification with boosting of extreme learning machine over arbitrarily partitioned data


Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extract- ing useful predictive modeling from these types of data sets is a challenging problem due to their high complexity. Analyzing large amount of streaming data that can be leveraged to derive business value is another complex prob- lem to solve. With high levels of data availability (i.e. Big Data) automatic classification of them has become an important and complex task. Hence, we explore the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) to build a predictive bag of classification models. Accordingly, (i) data set ensembles are created; (ii) ELM algorithm is used to build weak learners (classifier functions); and (iii) builds a strong learner from a set of weak learners. We applied this training model to the benchmark knowledge discovery and data mining data sets.

Soft Computing