PRE-PROCESSING AND POST-PROCESSING FOR ENHANCING KNOWLEDGE DISCOVERY USING SUPPORT VECTOR MACHINES

摘要

A system and method for enhancing knowledge discovery from data using alearning machine in general and a support vectormachine in particular. Training data for a learning machine is pre-processedin order to add meaning thereto. Pre-processing data mayinvolve transforming the data points and/or expanding the data points. Byadding meaning to the data, the learning machine is providedwith a greater amount of information for processing. With regard to supportvector machines in particular, the greater the amount ofinformation that is processed, the better generalizations about the data thatmay be derived. The learning machine is therefore trainedwith the pre-processed training data and is tested with test data that is pre-processed in the same manner. The test output from thelearning machine is post-processed in order to determine if the knowledgediscovered from the test data is desirable. Post-processinginvolves interpreting the test output into a format that may be compared withthe test data. Live data is pre-processed and input into thetrained and tested learning machine. The live output from the learning machinemay then be post-processed into a computationally derivedalphanumerical classifier for interpretation by a human or computer automatedprocess.