Feature Level Ensemble Method for Classifying Multi-Media Data

Saleh Alyahyan, Wenjia Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
18 Downloads (Pure)

Abstract

Multimedia data consists of several different types of data, such as numbers, text, images, audio etc. and they usually need to be fused or integrated before analysis. This study investigates a feature-level aggregation approach to combine multimedia datasets for building heterogeneous ensembles for classification. It firstly aggregates multimedia datasets at feature level to form a normalised big dataset, then uses some parts of it to generate classifiers with different learning algorithms. Finally, it applies three rules to select appropriate classifiers based on their accuracy and/or diversity to build heterogeneous ensembles. The method is tested on a multimedia dataset and the results show that the heterogeneous ensembles outperform the individual classifiers as well as homogeneous ensembles. However, it should be noted that, it is possible in some cases that the combined dataset does not produce better results than using single media data.
Original languageEnglish
Title of host publicationInternational Conference on Innovative Techniques and Applications of Artificial Intelligence
Subtitle of host publicationSGAI 2017: Artificial Intelligence XXXIV
EditorsMax Bramer, Miltos Petridis
PublisherSpringer
Pages235-249
Number of pages13
ISBN (Electronic)978-3-319-71078-5
ISBN (Print)978-3-319-71077-8
DOIs
Publication statusPublished - 21 Nov 2017
EventThirty-seventh SGAI International Conference on Artificial Intelligence. - CAMBRIDGE, Cambridge, United Kingdom
Duration: 12 Dec 201715 Dec 2017
Conference number: 37

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10630
ISSN (Print)0302-9743

Conference

ConferenceThirty-seventh SGAI International Conference on Artificial Intelligence.
Abbreviated titleAI2017
CountryUnited Kingdom
CityCambridge
Period12/12/1715/12/17

Keywords

  • Multimedia data mining
  • Feature level data aggregation
  • Diversity
  • Heterogeneous ensemble
  • Classification

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