Classification with Boosting of Extreme Learning Machine Over Arbitrarily Partitioned Data

TL;DR

This paper proposes a distributed AdaBoosting approach using Extreme Learning Machines over arbitrarily partitioned data to improve large-scale data classification, leveraging MapReduce for scalability and efficiency.

Contribution

It introduces a novel ensemble learning method combining ELM and AdaBoost over distributed data partitions using MapReduce, enhancing classification of big data.

Findings

Effective classification on benchmark datasets

Scalable approach for large-scale data

Improved accuracy with ensemble method

Abstract

Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extracting 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 problem to solve. With high levels of data availability (\textit{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.