Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Discovering Test Set Regularities in Relational Domains
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
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Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks
Proceedings of the 24th international conference on Machine learning
Multilabel classification via calibrated label ranking
Machine Learning
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Ranking and semi-supervised classification on large scale graphs using map-reduce
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Evolutionary approach for semantic-based query sampling in large-scale information sources
Information Sciences: an International Journal
Label-dependent node classification in the network
Neurocomputing
MapReduce approach to collective classification for networks
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Parallel processing of large graphs
Future Generation Computer Systems
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The problem of automated video categorization in large datasets is considered in the paper. A new Iterative Multi-label Propagation (IMP) algorithm for relational learning in multi-label data is proposed. Based on the information of the already categorized videos and their relations to other videos, the system assigns suitable categories--multiple labels to the unknown videos. The MapReduce approach to the IMP algorithm described in the paper enables processing of large datasets in parallel computing. The experiments carried out on 5-million videos dataset revealed the good efficiency of the multi-label classification for videos categorization. They have additionally shown that classification of all unknown videos required only several parallel iterations.