System and method for determining a behavior of a classifier for use with business data
A method for detecting change in business data using a statistical classifier process. The method includes inputting a first set of business data in a first format from a real business process from a first data source and storing the first set of business data into one or more memories. The method also includes inputting a second set of business data in a second format from a real business process from a second data source and storing the second set of business data into one or more memories. The method forms a statistical classifier by inputting the first set of business data into a learning process associating with the statistical classifier that processes business the data in the first format. The method stores the classifier into the one or more memories, the classifier being associated with the first set of data in the first format and processes the data from the first data source in the statistical classifier to derive a first result. The method also processes the data from the second data source in the statistical classifier to derive a second result and determines a behavior of the statistical classifier based upon at least the first result and the second result. The method displays information associated with the behavior of the statistical classifier.
1 . A method for detecting change in business data, the method comprising:
inputting a first set of business data in a first format from a real business process from a first data source;
storing the first set of business data into one or more memories;
inputting a second set of business data in a second format from a real business process from a second data source;
storing the second set of business data into one or more memories;
forming a statistical classifier by inputting the first set of business data into a learning process associating with the statistical classifier that processes business the data in the first format;
storing the classifier into the one or more memories, the classifier being associated with the first set of data in the first format;
processing the data from the first data source in the statistical classifier to derive a first result;
processing the data from the second data source in the statistical classifier to derive a second result;
determining a behavior of the statistical classifier based upon at least the first result and the second result; and
displaying information associated with the behavior of the statistical classifier.
2 . The method of claim 1 wherein the first data source and the second data source refer to a source at different points in time.
3 . The method of claim 1 wherein the first result is a pattern or a number result and the second result is a pattern or a number result.
4 . The method of claim 1 wherein the determining comprises comparing the first result with the second result.
5 . The method of claim 1 wherein the first format and the second format are the same format.
6 . The method of claim 1 wherein the behavior changes if the first result and the second result are substantially different.
7 . The method of claim 1 wherein the behavior changes if the first result and the second result do not change.
8 . The method of claim 1 wherein the displaying comprises outputting the information on a display.
9 . The method of claim 1 further comprising outputting the second result.
10 . A method for detecting change in business data, the method comprising:
inputting a first set of business data in a first format from a real business process from a first data source;
storing the first set of business data into one or more memories;
forming a statistical classifier by inputting the first set of business data in the first format into a learning process associated with the statistical classifier to process the first set of business data in the learning process;
storing the classifier into the one or more memories, the classifier being associated with the first set of data in the first format;
processing the data from the first data source in the statistical classifier to derive a first result;
processing the data from the nth data source in the statistical classifier to derive an nth result;
determining a behavior of the statistical classifier based upon at least the first result and the nth result;
outputting information associated with the behavior of the statistical classifier; and
repeating steps of inputting, storing, processing, and determining for other nth set of business data where n is greater than 1.
11 . A system performing the method of claim 10 .
12 . A method for detecting change in business data, the method comprising:
inputting a first set of business data in a first format from a real business process from a first data source;
storing the first set of business data into memory;
inputting a second set of business data in the first format from a real business process from a second data source;
storing the second set of business data into memory;
inputting a statistical classifier that processes business data in the first format;
storing the classifier into memory;
comparing the data from the first data source with the data from the second data source;
determining whether the comparison indicates that the behavior of the classifier when applied to business data from the business process is different for the two data sources;
displaying the result of the analysis.
13 . The method in 12 wherein the data sources correspond to time periods.
14 . The method in 13 wherein the first data source corresponds to an earlier time period and the second data source corresponds to a later time period.
15 . The method in 12 wherein the behavior of the classifier is some form of classification accuracy.
16 . The method in 15 wherein accuracy is measured by precision, recall or a combination thereof.
17 . The method in 12 wherein the behavior of the classifier is optimal classification performance.
18 . The method in 17 wherein optimal classification performance is measured by precision, recall or a combination thereof.
19 . The method in 12 wherein a gold set of human-labeled business data is created and the gold set is used as part of the determination as to whether the behavior of the classifier is different.
20 . The method in 12 wherein a metric is computed as part of the comparison and the metric is used as part of the determination as to whether the behavior of the classifier is different.
21 . The method in 20 wherein a threshold is computed and different behavior is predicted to occur if the metric is higher than the threshold.
22 . The method in 20 wherein several classifiers are investigated for behavior differences and the metric is used to rank the classifiers as to likelihood of different behavior.
23 . The method in 22 wherein the ranked list of classifiers is displayed to a user for further decision making.
24 . The method in 20 wherein the metric is proportion decrease.
25 . The method in 24 wherein proportion decrease is computed based on probabilistic predictions or discrete predictions.
26 . The method in 20 wherein the metric is proportion change.
27 . The method in 20 wherein proportion change is computed based on probabilistic predictions or discrete predictions.
28 . The method in 20 wherein the metric is small proportion.
29 . The method in 20 wherein small proportion is computed based on probabilistic predictions or discrete predictions.
30 . The method in 20 wherein the metric is distribution change.
31 . The method in 30 wherein distribution change is computed based on probabilistic predictions or discrete predictions.
32 . The method in 20 wherein the metric is similarity or dissimilarity of contingency table rows.
33 . The method in 32 wherein similarity or dissimilarity of contingency table rows is computed based on probabilistic predictions or discrete predictions.
34 . The method in 20 wherein the metric is distribution of good indicators in bad documents or a subset of bad documents.
35 . The method in 34 wherein distribution of good indicators in bad documents or a subset of bad documents is computed based on probabilistic predictions or discrete predictions.
36 . The method in 20 wherein the metric is similarity or dissimilarity of score distributions.
37 . The method in 36 wherein similarity or dissimilarity of score distributions is computed based on probabilistic predictions or discrete predictions.
38 . The method in 20 wherein the metric is a combination of two or more of proportion decrease, proportion change, small proportion, distribution change, similarity or dissimilarity of contingency table rows, distribution of good indicators in bad documents or a subset of bad documents, or similarity or dissimilarity of score distributions.
39 . The method in 12 wherein the two sets of business data are processed using a transformation such as duplicate elimination or near-duplicate elimination.
40 . The method in 12 wherein the data comprise text.