Classification with boosting of extreme learning machine over arbitrarily partitioned data

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 (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.

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave

“…AdaBoost is a supervised learning algorithm for solving classification problems [ 31 ]. In each sequence, misclassified instances are given more weight for the next sequence while correctly classified instances are given lower weight.…”

Machine learning on big data: Opportunities and challenges

“…Nevertheless, scaling up ensemble learning techniques to large and distributed data remains a research challenge. Adaboosting of Extreme Learning Machine has been explored by leveraging the power of MapReduce to build a reliable predictive bag of classification models [72]. These models can produce good generalization performance and efficiency.…”

Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review

“…Researchers agree that more research is needed to find novel ways to survive the big data revolution [32] and learn the best practices to extract knowledge from unstructured data [34]. Automatic classification of large-scale datasets significantly contributes to deriving business value [35].…”