Abstract
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.