Considering Currency in Decision Trees in the Context of Big Data

摘要: In the current age of big data, decision trees are one most commonly applied data mining methods. However, for reliable results they require up-to-date input which is not always given in reality. We present a two-phase approach based on probability theory considering currency stored trees. Our efficient and thus suitable applications. Moreover, it independent particular tree classifier. Finally, context-specific since structure supplemental taken into account. demonstrate benefits novel by applying to three datasets. The show substantial increase classification success rate as opposed currency. Thus, our prevents wrong consequently decisions.