The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In imbalanced learning, most standard classification algorithms usually fail to properly represent data distribution and provide unfavorable classification performance. More specifically, the decision rule of minority class is usually weaker than majority class, leading to many misclassification of expensive minority class data. Motivated by our previous work ADASYN [1], this paper presents a novel...
We investigate the kernel density estimation (KDE) problem with stream data in this paper. Specifically, we analyze the characteristics of stream data density estimation, and propose an approach based on self-organizing map (SOM) to tackle the challenges of traditional KDE techniques for stream data analysis, such as computational cost, processing time, and memory requirement. Our proposed approach...
Most media access control (MAC) protocols can be classified as contention based or controlled based according to their transmission mechanisms. To classify contention based or control based MAC protocols in an unknown primary network, we choose received power mean and variance as two features for support vector machines (SVMs) in a machine learning based algorithm. The data consisting of these two...
This paper proposes a novel feature ranking method, DensityRank, based on kernel estimation on the feature spaces to improve the classification performance. As the availability of raw data in many of today's applications continues to grow at an explosive rate, it is critical to assess the learning capabilities of different features and select the important subset of features to improve learning accuracy...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.