A scalable expressive ensemble learning using random prism: a MapReduce approach.

Stahl, Frederic; May, David; Mills, Hugo; Bramer, Max; Gaber, Mohamed Medhat

Abstract

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system.

Citation

STAHL, F., MAY, D., MILLS, H., BRAMER, M. and GABER, M.M. 2015. A scalable expressive ensemble learning using random prism: a MapReduce approach. In Hameurlain, A., Küng, J., Wagner, R., Sakr, S., Wang, L. and Zomaya, A. (eds.) Transactions on large-scale data- and knowledge-centred systems XX: special issue on advanced techniques for big data management. Lecture notes in computer science, 9070. Berlin: Springer [online], pages 90-107. Available from: https://doi.org/10.1007/978-3-662-46703-9_4

• Acceptance Date: Mar 18, 2015
• Online Publication Date: Mar 18, 2015
• Publication Date: Mar 18, 2015
• Deposit Date: Nov 23, 2015
• Publicly Available Date: Mar 19, 2016
• Print ISSN: 0302-9743
• Publisher: Springer
• Pages: 90-107
• Series Title: Lecture notes in computer science
• Series Number: 9070
• Series ISSN: 0302-9743
• Book Title: Transactions on large-scale data- and knowledge-centred systems XX: special issue on advanced techniques for big data management.
• ISBN: 9783662467022
• DOI: https://doi.org/10.1007/978-3-662-46703-9_4
• Keywords: Random Forest; Base Classifier; Computing Node; Training Instance; Ensemble Classifier
• Public URL: http://hdl.handle.net/10059/1360