Ensemble of classifiers for ontology enrichment

A classifier is a basis of ontology learning systems. Classification of text documents is used in many applications, such as information retrieval, information extraction, definition of spam. A new ensemble of classifiers based on SVM (a method of support vectors), LSTM (neural network) and word emb...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1015; no. 3; pp. 32123 - 32129
Main Authors Semenova, A V, Kureichik, V M
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.05.2018
Subjects
Classification
Classifiers
Information retrieval
Neural networks
Ontology
Physics
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Summary:A classifier is a basis of ontology learning systems. Classification of text documents is used in many applications, such as information retrieval, information extraction, definition of spam. A new ensemble of classifiers based on SVM (a method of support vectors), LSTM (neural network) and word embedding are suggested. An experiment was conducted on open data, which allows us to conclude that the proposed classification method is promising. The implementation of the proposed classifier is performed in the Matlab using the functions of the Text Analytics Toolbox. The principal difference between the proposed ensembles of classifiers is the high quality of classification of data at acceptable time costs.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1015/3/032123