NDLI: Scalable Learning of k-dependence Bayesian Classifiers under MapReduce

Content Provider	IEEE Xplore Digital Library
Author	Arias, J. Gamez, J.A. Puerta, J.M.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Syst., Univ. of Castilla-La Mancha, Albacete, Spain (Arias, J.; Gamez, J.A.; Puerta, J.M.)
Abstract	In Data Mining there is a constant need to provide more scalable tools in order to tackle new domains with an increased level of complexity. Over the last few years one of the main challenges in this field is the growing size of the available data, owing to the level of data generation and storage capacities provided by new emergent technology, a range of new computational paradigms and parallel architectures have been proposed. MapReduce got the leading role in the field of Big Data applications since its appearance, and many popular Data Analysis tools and techniques have been successfully adapted to this paradigm. Supervised classification is one of the most common problems in Data Mining, and Bayesian Networks Classifiers (BNC) have become one of the most extended and competitive techniques to approach them. In this paper we propose a parallel definition of the KDB (k-dependence Bayesian classifier) algorithm under the MapReduce framework. We focus on obtaining maximum scalability and flexibility by exploring the concepts of vertical and horizontal parallelism, thus addressing both Big Data and High Dimensional problems simultaneously. We analyse its properties and the advantages of applying it to large datasets of different nature. Finally, an experimental evaluation is performed by testing a Hadoop implementation of our proposal on a high-end cluster of computers.
Starting Page	25
Ending Page	32
File Size	353779
Page Count	8
File Format	PDF
e-ISBN	9781467379526
DOI	10.1109/Trustcom.2015.558
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-20
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Scalability Big data Bayes methods Complexity theory Data mining Niobium
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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