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.
With the rapid development of power grid, prediction of electric quantity changes has become increasingly important. High-performance power grid systems can improve economic effectiveness and operational efficiency through accurate prediction. This paper proposes a prediction model based on temperature, humidity, time, and the number of people. On account of the standards of support vector machine...
More and more power plants have been constructed and generated by intermittent energy. As a clean and renewable energy, such sources as wind and solar are favored in the new generation of power grid system. However, influenced by factors of geography, circumstance and climates, the renewable energy has the characteristics of intermittency, volatility and uncontrollability, which reduce the efficient...
Today, big data is not only the data scenario with large volume, but also high-speed and changing all the time. Such data streams commonly exist in Smart Grid facilities. Decision tree as one of the most widely-used analysis methods, has been applied in the decision support system for smart grid. This paper proposes a two-level classifier combining cache-based classifier and incremental decision tree...
Decision tree, as one of the most widely used methods in data mining, has been used in many realistic application. Incremental decision tree handles streaming data scenario that is applicable for big data analysis. However, imperfect data are unavoidable in real-world applications. Studying the state-of-art incremental decision tree induction using Hoeffding bound, we investigated the influence of...
Hoeffding's bound (HB) has been widely used for node splitting in incremental decision tree algorithms. Many decision-tree algorithms adopt a sliding-window technique to detect concept drift when mining changing data streams. This paper presents a novel node-splitting approach that replaces the traditional HB with a new measure. The new measure is derived from a loss function applied in a cache-based...
How to efficiently uncover the knowledge hidden within massive and big data remains an open problem. One of the challenges is the issue of 'concept drift' in streaming data flows. Concept drift is a well-known problem in data analytics, in which the statistical properties of the attributes and their target classes shift over time, making the trained model less accurate. Many methods have been proposed...
Very Fast Decision Tree (VFDT) is an exemplar of classification techniques in data stream mining where models are built by incremental learning from continuously arriving data instead of batches. Many variations and modifications were made upon VFDT since it was first introduced in year 2000. Novel contributions were mainly made in two aspects of VFDT, tree induction process and prediction process,...
Very Fast Decision Tree (VFDT) in data stream mining has been widely studied for more than a decade. VFDT in essence can mine over a portion of an unbounded data stream at a time, and the structure of the decision tree gets updated whenever new data feed in; hence it can predict better upon the input of fresh data. Inherent from traditional decision trees that use information gains for tree induction,...
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.