Classifying attribute data intervals

摘要

The present techniques extract attribute data of one or more classified members for one or more user attributes. With respect to a particular user attribute of the one or more user attributes, the present techniques determine initial attribute data intervals corresponding to the particular user attribute based on attribute data and classes of the classified members from the extracted attribute data. With respect to a classified member whose attribute data is missing for the particular user attribute, the present techniques set the attribute data as a preset missing value. The present techniques then merge the preset missing value into each of the initial user attribute data intervals and calculate a Maximum Posteriori Probability (MAP) Bayes estimate value respectively, and determine initial user attribute data intervals with a smallest MAP Bayes estimated value as final attribute data intervals corresponding to the particular user attribute.

主权项

1. A method performed by one or more processors configured with computer-executable instructions, the method comprising:
extracting attribute data of one or more classified members for one or more user attributes; with respect to a particular user attribute of the one or more user attributes,
determining one or more initial user attribute data intervals corresponding to the particular user attribute based on attribute data and classes of the classified members from the extracted attribute data;with respect to a classified member whose attribute data is missing for the particular user attribute, setting attribute data of the classified member as a preset missing value;merging the preset missing value into each of the one or more initial user attribute data intervals respectively;calculating a Maximum Posteriori Probability (MAP) Bayes estimate value each time when the preset missing value is merged into each of the one or more initial user attribute data intervals respectively; anddetermining initial user attribute data intervals with a smallest MAP Bayes estimated value as final attribute data intervals corresponding to the particular user attribute.