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Two Stage SVM and kNN Text Documents Classifier

Abstract

The paper presents an approach to the large scale text documents classification problem in parallel environments. A two stage classifier is proposed, based on a combination of k-nearest neighbors and support vector machines classification methods. The details of the classifier and the parallelisation of classification, learning and prediction phases are described. The classifier makes use of our method named one-vs-near. It is an extension of the one-vs-all approach, typically used with binary classifiers in order to solve multiclass problems. The experiments were performed on a large scale dataset, with use of many parallel threads on a supercomputer. Results of the experiments show that the proposed classifier scales well and gives reasonable quality results. Finally, it is shown that the proposed method gives better performance compared to the traditional approach.

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Details

Category:
Conference activity
Type:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Title of issue:
Pattern Recognition and Machine Intelligence strony 279 - 289
Language:
English
Publication year:
2015
Bibliographic description:
Kępa M., : Two Stage SVM and kNN Text Documents Classifier// Pattern Recognition and Machine Intelligence/ : , 2015, s.279-289
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-319-19941-2_27
Verified by:
Gdańsk University of Technology

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