A scalable expressive ensemble learning using Random Prism: a MapReduce approach


Autoria(s): Stahl, Frederic; May, David; Mills, Hugo; Bramer, Max; Gaber, Mohamed Medhat
Data(s)

2015

Resumo

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.

Formato

text

Identificador

http://centaur.reading.ac.uk/39793/1/typeinst.pdf

Stahl, F. <http://centaur.reading.ac.uk/view/creators/90005065.html>, May, D., Mills, H., Bramer, M. and Gaber, M. M. (2015) A scalable expressive ensemble learning using Random Prism: a MapReduce approach. Transactions on Large-Scale Data- and Knowledge-Centered Systems , 9070. pp. 90-107. doi: 10.1007/978-3-662-46703-9_4 <http://dx.doi.org/10.1007/978-3-662-46703-9_4> (LNCS)

Idioma(s)

en

Publicador

Springer Berlin Heidelberg

Relação

http://centaur.reading.ac.uk/39793/

creatorInternal Stahl, Frederic

http://dx.doi.org/10.1007/978-3-662-46703-9_4

10.1007/978-3-662-46703-9_4

Tipo

Article

PeerReviewed