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An Ensemble Method for Large Scale M...

Liu, Xuan.

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An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce./
Author:	Liu, Xuan.
Description:	133 p.
Notes:	Source: Masters Abstracts International, Volume: 52-06.
Contained By:	Masters Abstracts International52-06(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MS26654
ISBN:	9780499266545

An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce.
Liu, Xuan.

An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce. - 133 p.

Source: Masters Abstracts International, Volume: 52-06.

Thesis (M.A.Sc.)--University of Ottawa (Canada), 2014.

We propose a new ensemble algorithm: the meta-boosting algorithm. This algorithm enables the original Adaboost algorithm to improve the decisions made by different WeakLearners utilizing the meta-learning approach. Better accuracy results are achieved since this algorithm reduces both bias and variance. However, higher accuracy also brings higher computational complexity, especially on big data. We then propose the parallelized meta-boosting algorithm: Parallelized-Meta-Learning (PML) using the MapReduce programming paradigm on Hadoop. The experimental results on the Amazon EC2 cloud computing infrastructure show that PML reduces the computation complexity enormously while retaining lower error rates than the results on a single computer. As we know MapReduce has its inherent weakness that it cannot directly support iterations in an algorithm, our approach is a win-win method, since it not only overcomes this weakness, but also secures good accuracy performance. The comparison between this approach and a contemporary algorithm AdaBoost.PL is also performed.

ISBN: 9780499266545Subjects--Topical Terms:

626642
Computer Science.

An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce.
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245 1 3 $a An Ensemble Method for Large Scale Machine Learning with Hadoop MapReduce.
300 $a 133 p.
500 $a Source: Masters Abstracts International, Volume: 52-06.
502 $a Thesis (M.A.Sc.)--University of Ottawa (Canada), 2014.
520 $a We propose a new ensemble algorithm: the meta-boosting algorithm. This algorithm enables the original Adaboost algorithm to improve the decisions made by different WeakLearners utilizing the meta-learning approach. Better accuracy results are achieved since this algorithm reduces both bias and variance. However, higher accuracy also brings higher computational complexity, especially on big data. We then propose the parallelized meta-boosting algorithm: Parallelized-Meta-Learning (PML) using the MapReduce programming paradigm on Hadoop. The experimental results on the Amazon EC2 cloud computing infrastructure show that PML reduces the computation complexity enormously while retaining lower error rates than the results on a single computer. As we know MapReduce has its inherent weakness that it cannot directly support iterations in an algorithm, our approach is a win-win method, since it not only overcomes this weakness, but also secures good accuracy performance. The comparison between this approach and a contemporary algorithm AdaBoost.PL is also performed.
590 $a School code: 0918.
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710 2 $a University of Ottawa (Canada). $b Electrical Engineering and Computer Science. $3 2092550
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MS26654