1. A non-transitory computer readable medium configured to: cause a processor to classify a new record by assigning a dynamic weight to at least one attribute of the new record and using the dynamic weight of the at least one attribute to classify the new record; determine a static weight for each of a plurality of attributes in a data set wherein the data set includes a plurality of records and each record comprises one of a plurality of descriptors for each of said plurality of attributes; and cause the processor to determine an interval of variation about each said static weight, wherein said interval of variation for each of said plurality of attributes is determined by finding a conditional entropy for each of the plurality of descriptors for said attribute, inverting each conditional entropy to an attribute value importance score to form a group of attribute value importance scores, dividing the group of attribute value importance scores into quartiles, calculating a lower bound scaling factor for the interval of variation as 
 LBSF i =( Q 2− Q 1)/ Q 2 And an upper bound scaling factor for the interval of variation as 
 UBSF i =( Q 3− Q 2)/ Q 2 And a lower bound as 
 LB i =W i −W i *LBSF i , And an upper bound as 
 UB i =W i +W i *UBSF i .