Query Join Processing over Uncertain Data for Decision Tree Classifiers

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Provided by: International Journal of Computer Science & Engineering Technology (IJCSET)
Topic: Big Data
Format: PDF
Traditional decision tree classifiers work with the data whose values are known and precise. The authors can also extend those classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty measurement/quantization errors, data staleness, and multiple repeated measurements. Rather than abstracting uncertain data by statistical derivatives, such as mean and median, the accuracy of a decision tree classifier can be improved much if the complete information of a data item is used by utilizing the Probability Density Function (PDF).
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