A New Method for Image Classification by Using Multilevel Association Rules
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Challenges and Interesting Research Directions in Associative Classification
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Integrated Generic Association Rule Based Classifier
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Association Rule Mining with Dynamic Adaptive Support Thresholds for Associative Classification
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Correlation-Based Video Semantic Concept Detection Using Multiple Correspondence Analysis
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
Video semantic concept detection using ontology
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Weighted Association Rule Mining for Video Semantic Detection
International Journal of Multimedia Data Engineering & Management
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
International Journal of Multimedia Data Engineering & Management
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Associative classification (AC) has been studied in the areas of content-based multimedia retrieval and semantic concept detection due to its high accuracy. The traditional AC algorithm discovers the association rules with the frequency count (minimum support) and ranking threshold (minimum confidence) while restricted to the concepts (class labels). In this paper, we propose a novel framework with a new associative classification algorithm which generates the classification rules based on the correlation between different feature-value pairs and the concept classes by using Multiple Correspondence Analysis (MCA). Experimenting with the high-level features and benchmark data sets from TRECVID, our proposed algorithm achieves promising performance and outperforms three well-known classifiers which are commonly used for performance comparison in the TRECVID community.