Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.
Scalable Video Coding is the latest extension of the famous Advance Video Coding standard. The main advantage of SVC is that it can provide scalability for visual services which are serving customers with heterogeneous network conditions and terminals' capabilities. Nevertheless, the multimedia service research community and industry have not been able to fully utilize the entire potential of this video coding standard extension. One important reason is because of the lack of an evaluation tool-set and platform widely available for usage in the designing, evaluating as well as deploying processes of SVC-based visual services. EvalSVC aims to fill this gap and fosters SVC-based applications and research in multimedia services. It is capable of evaluating the enhanced features (such as spatial, temporal, SNR, and combined scalability) of SVC bit-streams transmitting over real or simulated networks. This tool-set is publicly available.
The Prism family is an alternative set of predictive data mining algorithms to the more established decision tree data mining algorithms. Prism classifiers are more expressive and user friendly compared with decision trees and achieve a similar accuracy compared with that of decision trees and even outperform decision trees in some cases. This is especially the case where there is noise and clashes in the training data. However, Prism algorithms still tend to overfit on noisy data; this has led to the development of pruning methods which have allowed the Prism algorithms to generalise better over the dataset. The work presented in this paper aims to address the problem of overfitting at rule induction stage for numerical attributes by proposing a new numerical rule term structure based on the Gauss Probability Density Distribution. This new rule term structure is not only expected to lead to a more robust classifier, but also lowers the computational requirements as it needs to induce fewer rule terms.
Abstract-Scalable Video Coding is the multi-layer extension of Advanced Video Coding with the advantage of providing visual services for customers with heterogeneous network conditions and terminals' capabilities. In this research, advanced features of Scalable Video Coding are investigated and compared with Advanced Video Coding. A new video transmission evaluation platform is proposed to support the latest Network Abstract Layer Units of Scalable Video Coding. A new interface between the scalable video evaluation platform and an overlay simulation platform is developed so that the transmission performance of Scalable Video Coding bit-streams on an overlay network will be evaluated. Both structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) measurements are applied to evaluate the performance of the video transmission session. New measurement results are also provided so that other SVC-based service designers can select the right video scalability for their service.
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