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The maturity of the smartphone and the World Wide Web (www) technologies have driven many social network applications which have facilitated people to share text and multimedia contents. The social networks that facilitate users to share the check-in (location visit) information are known as the location-based social networks (LBSN)s and provide various information for a recommendation problem that...
Multimedia semantic concept detection is an emerging research area in recent years. One of the prominent challenges in multimedia concept detection is data imbalance. In this study, a multimedia data mining framework for interesting concept detection in videos is presented. First, the Minimum Description Length (MDL) discretization algorithm is extended to handle the imbalanced data. Thereafter, a...
Recent developments in social media and cloud storage lead to an exponential growth in the amount of multimedia data, which increases the complexity of managing, storing, indexing, and retrieving information from such big data. Many current content-based concept detection approaches lag from successfully bridging the semantic gap. To solve this problem, a multi-stage random forest framework is proposed...
Numerous classification algorithms have been developed for a variety of data types. However, it is nearly impossible for one classifier to perform the best in all kinds of datasets. Therefore, ensemble learning models which aim to take advantages of different classifiers have received a lot of attentions recently. In this paper, a scalable classifier ensemble framework assisted by a set of judgers...
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