Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/62942

TitleA text mining based supervised learning algorithm for classification of manufacturing suppliers
Author(s)Manupati, V. K.
Akhtar, M. D.
Varela, M.L.R.
Putnik, Goran D.
Trojanowska, J.
Machado, José
KeywordsClassification algorithm
Support vector machine
Text mining
Issue date2018
PublisherSpringer-Verlag
JournalAdvances in Intelligent Systems and Computing
Abstract(s)With the expeditious growth of unstructured massive data on the World Wide Web (WWW), more advanced tools, techniques, methods, and approaches for information organization and retrieval are desired. Text mining is one such approach to achieve the above mentioned demand. One of the main text mining applications is how to classify data presented by different industries into groups. In this paper, the classification of data into various groups based on the choice of the users at any given point of time is proposed. Here, a support vector machine (SVM) based classification algorithm is established to classify the text data into two broad categories of Manufacturing and Non-Manufacturing suppliers. Later, the performance of the proposed classifier was tested experimentally using most commonly used accuracy measures such as precision, recall, and F-measure. Results proved the efficiency of the proposed approach for classification of the texts.
TypeConference paper
URIhttp://hdl.handle.net/1822/62942
ISBN9783319776996
DOI10.1007/978-3-319-77700-9_24
ISSN2194-5357
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

Files in This Item:
File Description SizeFormat 
WORLDCIST MANUSCRIPT_final.pdf
  Restricted access
474,56 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID