Extending Decision Tree Clasifiers for Uncertain Data

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Provided by: IJESAT (International Journal of Engineering Science & Advanced Technology)
Topic: Big Data
Format: PDF
Traditionally, decision tree classifiers work with data whose values are known and precise. The authors extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting uncertain data by statistical derivatives (such as mean and median).
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