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Metalearning is a technique, which enables us to improve classification accuracy in Data Mining. It uses several classifiers to compute final category for a test sample. The most popular metalearning methods are Bagging and Boosting. Effectiveness of these methods with the usage of decision tree (SPRINT) has been tested and presented in this paper.
Data mining or Knowledge discovery is seen as an increasingly important tool by modern business to transform data into an informational advantage. Mining is a process of finding correlations among dozens of fields in large relational databases and extracts useful information that can be used to increase revenue, cuts costs, or both. Classification is a supervised machine learning procedure and an...
The SPRINT algorithm describes a distributed way to construct a decision tree for classification in large data sets. It can be applied to in-network classification tree construction. The costly data transfer of sensor data to the sink can be avoided while execution time is still acceptable. The SPRINT algorithm and its extensions are introduced. Furthermore, different scenarios that implement classification...
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