Infoglide Software Corporation

International Business Machines Corporation

Vasken Torossian

Charles Moon

Harry Roger Kolar

Frederick J Scheibl

Kevin Eric Norsworthy

Michael Zrubek

Julio Ortega

Quan G Cung

Ben Peter Yuhas

The invention provides a classification engine for classifying documents that makes use of functions included in a similarity search engine. The classification engine executes a classify command from a client that makes use of similarity search results, and rules files, classes files, and a classification profile embedded in the classification command. When the classification receives a classify command from a client, it retrieves a classification profile and input documents to be classified, sends extracted values from the input documents based on anchor values to a XML transformation engine to obtain a search schema, requests a similarity search by a search manager to determine the similarity between input documents and anchor values, and classifies the input documents according to the rules files, classes files, and the classification profile. The client is then notified that the classify command has been completed and the classification results are stored in a database.

System and method for classification of documents

Attributes of a data set to be employed in generating a predictive model are analyzed based on entropy, chi-square, or similar statistical measure. A target group of samples exhibiting one or more desired attributes is identified, then remaining attribute values for the target group are compared to corresponding attribute values for the whole sample population. A subset of all available attributes is then selected from those attributes which exhibit, when comparing attribute values of target group samples to attribute values for the whole sample population, the greatest relative difference or divergence. This subset is employed to generate the predictive model. Efficiency in generating the predictive model and the accuracy of the resulting predictive model is improved, since fewer attributes are employed and less computational resources are required.

Method for computing models based on attributes selected by entropy

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Round Rock, TX, United States of America

Vasken Torossian