| 摘要 |
A classification model is trained and used for detecting patterns in input data. The training of the model includes retrieving a set of previously recorded input data containing a plurality of items associated with a plurality of entities and adding to each entity a known classification. Furthermore, training the model includes determining rules from the set of previously recorded input data and the known classification by associating the classification of each entity with the respective items of said entity. The training of the model further includes determining a set of rules which are applicable, aggregating the lift values of the rules determined for said entity, and predicting a classification based on the aggregated association values for each entity. The resulting aggregated lift value together with the respective entity and classification are used as input for a standard classification algorithm, where the result is a classification model. |
