DATA CLASSIFICATION METHODS USING MACHINE LEARNING TECHNIQUES

• US 20080086433A1
• Filed: 05/23/2007
• Published: 04/10/2008
• Est. Priority Date: 07/12/2006
• Status: Active Grant

• Current Assignee: Kofax Incorporated
• Original Assignee: Kofax Incorporated
• Inventors: Borrey, Roland, Schmidtler, Mauritius A.R.
• Granted Patent: US 7,937,345 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]

Abstract

A method for adapting to a shift in document content according to one embodiment of the present invention includes receiving at least one labeled seed document; receiving unlabeled documents; receiving at least one predetermined cost factor; training a transductive classifier using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents; classifying the unlabeled documents having a confidence level above a predefined threshold into a plurality of categories using the classifier; reclassifying at least some of the categorized documents into the categories using the classifier; and outputting identifiers of the categorized documents to at least one of a user, another system, and another process. Methods for separating documents are also presented. Methods for document searching are also presented.

Claims

1. A method for adapting to a shift in document content, comprising:

receiving at least one labeled seed document;

receiving unlabeled documents;

receiving at least one predetermined cost factor;

training a transductive classifier using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents;

classifying the unlabeled documents having a confidence level above a predefined threshold into a plurality of categories using the classifier;

reclassifying at least some of the categorized documents into the categories using the classifier; and

outputting identifiers of the categorized documents to at least one of a user, another system, and another process.

2. The method of claim 1, further comprising moving an unlabeled document having a confidence level below the predefined threshold into one or more new categories.

3. The method of claim 1, and further comprising training the transductive classifier through iterative calculation using at least one predetermined cost factor, the at least one seed document, and the unlabeled documents, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value, and using the trained classifier to classify the unlabeled documents.

4. The method of claim 3, further comprising receiving a data point label prior probability for the seed document and unlabeled documents, wherein for each iteration of the calculations the data point label prior probability is adjusted according to an estimate of a data point class membership probability.

5. The method of claim 1, wherein the unlabeled documents are customer complaints, and further comprising linking product changes with customer complaints.

6. The method of claim 1, wherein the unlabeled documents are invoices.

7. A method for separating documents, comprising:

receiving labeled data;

receiving a sequence of unlabeled documents;

adapting probabilistic classification rules using transduction based on the labeled data and the unlabeled documents;

updating weights used for document separation according to the probabilistic classification rules;

determining locations of separations in the sequence of documents;

outputting indicators of the determined locations of the separations in the sequence to at least one of a user, another system, and another process; and

flagging the documents with codes, the codes correlating to the indicators.

8. A method for document searching, comprising:

receiving a search query;

retrieving documents based on the search query;

outputting the documents;

receiving user-entered labels for at least some of the documents, the labels being indicative of a relevance of the document to the search query;

training a classifier based on the search query and the user-entered labels;

performing a document classification technique on the documents using the classifier for reclassifying the documents; and

outputting identifiers of at least some of the documents based on the classification thereof.

9. The method of claim 8, wherein the document classification technique includes a transductive process.

10. The method of claim 9, wherein the classifier is a transductive classifier, and further comprising training the transductive classifier through iterative calculation using at least one predetermined cost factor, the search query, and the documents, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value, and using the trained classifier to classify the documents.

11. The method of claim 10, further comprising receiving a data point label prior probability for the search query and documents, wherein for each iteration of the calculations the data point label prior probability is adjusted according to an estimate of a data point class membership probability.

12. The method of claim 8, wherein the document classification technique includes a support vector machine process.

13. The method of claim 8, wherein the document classification technique includes a maximum entropy discrimination process.

14. The method of claim 8, wherein the reclassified documents are output, those documents having a highest confidence being output first.