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Ensemble of Neural Networks for Multi-label Document Classification

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932759" target="_blank" >RIV/49777513:23520/17:43932759 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Ensemble of Neural Networks for Multi-label Document Classification

  • Original language description

    This paper deals with multi-label document classification using an ensemble of neural networks. The assumption is that different network types can keep complementary information and that the combination of more neural classifiers will bring higher accuracy. We verify this hypothesis by an error analysis of the individual networks. One contribution of this work is thus evaluation of several network combinations that improve performance over one single network. Another contribution is a detailed analysis of the achieved results and a proposition of possible directions of further improvement. We evaluate the approaches on a Czech ČTK corpus and also compare the results with state-of-the-art approaches on the English Reuters-21578 dataset. We show that the ensemble of neural classifiers achieves competitive results using only very simple features.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    ITAT 2017: Information Technologies—Applications and Theory Proceedings of the 17th conference ITAT 2017

  • ISBN

    978-1-974274-74-1

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    186-192

  • Publisher name

    CreateSpace Independent Publishing Platform, 2017

  • Place of publication

  • Event location

    Martinské hole, Slovakia

  • Event date

    Sep 22, 2017

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article