COMING SOON! PQDT Open is getting a new home!

ProQuest Open Access Dissertations & Theses will remain freely available as part of a new and enhanced search experience at

Questions? Please refer to this FAQ.

Dissertation/Thesis Abstract

An efficient approach to machine learning based text classification through distributed computing
by Immaneni, Raghu Nandan, M.S., California State University, Long Beach, 2015, 86; 1603338
Abstract (Summary)

Text Classification is one of the classical problems in computer science, which is primarily used for categorizing data, spam detection, anonymization, information extraction, text summarization etc. Given the large amounts of data involved in the above applications, automated and accurate training models and approaches to classify data efficiently are needed.

In this thesis, an extensive study of the interaction between natural language processing, information retrieval and text classification has been performed. A case study named “keyword extraction” that deals with ‘identifying keywords and tags from millions of text questions’ is used as a reference. Different classifiers are implemented using MapReduce paradigm on the case study and the experimental results are recorded using two newly built distributed computing Hadoop clusters. The main aim is to enhance the prediction accuracy, to examine the role of text pre-processing for noise elimination and to reduce the computation time and resource utilization on the clusters.

Indexing (document details)
Advisor: Englert, Burkhard
Commitee: Aliasgari, Mehrdad, Tankelevich, Roman
School: California State University, Long Beach
Department: Computer Engineering and Computer Science
School Location: United States -- California
Source: MAI 55/02M(E), Masters Abstracts International
Subjects: Computer science
Keywords: Hadoop, Keyword extraction, Map reduce, Svm, Text classification, Tf-idf
Publication Number: 1603338
ISBN: 978-1-339-21495-5
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy