Decision level ensemble method for classifying multi-media data

Saleh Alyahyan, Wenjia Wang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
50 Downloads (Pure)


In the digital era, the data, for a given analytical task, can be collected in different formats, such as text, images and audio etc. The data with multiple formats are called multimedia data. Integrating and fusing multimedia datasets has become a challenging task in machine learning and data mining. In this paper, we present heterogeneous ensemble method that combines multi-media datasets at the decision level. Our method consists of several components, including extracting the features from multimedia datasets that are not represented by features, modelling independently on each of multimedia datasets, selecting models based on their accuracy and diversity and building the ensemble at the decision level. Hence our method is called decision level ensemble method (DLEM). The method is tested on multimedia data and compared with other heterogeneous ensemble based methods. The results show that the DLEM outperformed these methods significantly.
Original languageEnglish
Pages (from-to)1219–1227
Number of pages9
JournalWireless Networks
Issue number3
Early online date11 Dec 2018
Publication statusPublished - Apr 2022
Event9th EAI International Conference on Big Data Technologies and Applications - Exeter, United Kingdom
Duration: 4 Sep 20185 Sep 2018


  • Multi-media data
  • Machine learning
  • Ensemble methods
  • Data fusion
  • decision fusion
  • Classification
  • Diversity
  • Ensemble
  • Decision level fusion
  • Models selection

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