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Accurate network traffic classification is significant to numerous network activities, such as QoS and network management etc. While port-based or payload-based classification methods are becoming more and more difficult, Machine Learning methods are promising in many aspects. In this paper, we improve the standard Random Forest by setting the variable selection probability according to the importance...
Location positioning in indoor environments is a major challenge. Various algorithms have been developed over years to address the problem of indoor positioning. One of the most cost effective choice for indoor positioning is based on received signal strength indicator (RSSI) using existing Wi-Fi networks in commercial and/or public areas. This solution is infrastructure-free and offers meter-range...
Attribute space extension for decision forests often contribute to improving the ensemble accuracy. In this paper we suggest the use of a recent method for attribute space extension where the newly generated attributes that have high classification capacity are chosen for extension. In literature, it is shown that the inclusion of these new attributes in the attribute space increases the prediction...
Many problems in bioinformatics involve high-dimensional, difficult-to-process collections of data. For example, gene micro arrays can record the expression levels of thousands of genes, many of which have no relevance to the underlying medical or biological question. Building classification model son such datasets can thus take excessive computational time and still give poor results. Many strategies...
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