Apparatus and method for selecting a working data set for model development

• US 5,809,490 A
• Filed: 05/03/1996
• Issued: 09/15/1998
• Est. Priority Date: 05/03/1996
• Status: Expired due to Fees

• Current Assignee: Aspentech Corporation
• Original Assignee: Aspen Technology (Telefonaktiebolaget LM Ericsson)
• Inventors: Guiver, John P., Klimasauskas, Casimir C.
• Primary Examiner(s): Voeltz, Emanuel Todd
• Assistant Examiner(s): Smithers, Matthew
• Application Number:
  US08/642,779
• Time in Patent Office: 865 Days
• Field of Search: 395/11, 395/24, 395/22, 395/23
• US Class Current: 706/16
• CPC Class Codes: G06F 18/10 Pre-processing; Data cleansing
• CPC Class Codes: G06F 18/2137 based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning

Abstract

The present invention provides a data selection apparatus which augments a set of training examples with the desired output data. The resulting augmented data set is normalized such that the augmented data values range between -1 and +1 and such that the mean of the augmented data set is zero. The data selection apparatus then groups the augmented and normalized data set into related clusters using a clusterizer. Preferably, the clusterizer is a neural network such as a Kohonen self-organizing map (SOM). The data selection apparatus further applies an extractor to cull a working set of data from the clusterized data set. The present invention thus picks, or filters, a set of data which is more nearly uniformly distributed across the portion of the input space of interest to minimize the maximum absolute error over the entire input space. The output of the data selection apparatus is provided to train the analyzer with important sub-sets of the training data rather than with all available training data. A smaller training data set significantly reduces the complexity of the model building or analyzer construction process.

Claims

1. A method for generating a working set of data from a full set of data in a computer for building an analyzer, said analyzer having target outputs, said method comprising the steps of:

augmenting said full set of data with the target outputs;

normalizing said augmented data;

clustering said augmented and normalized data; and

selecting one or more members of said clusterized data as said working set of data.

2. The method of claim 1, wherein said clustering step further comprises the step of selecting a subset of said augmented and normalized data.

3. The method of claim 1, wherein said augmenting step further comprises the step of selecting a portion of said full set of data and a portion of said target outputs.

4. The method of claim 1, further comprising the step of applying univariate, non-linear transformations to said augmented and normalized data prior to said clustering step.

5. The method of claim 1, wherein said clustering step uses a weighted distribution.

6. The method of claim 1, wherein said clustering step uses an equi-probable distribution.

7. The method of claim 1, wherein said clustering step further comprises the step of emphasizing said outputs.

8. The method of claim 1, wherein said clustering step uses a statistical technique.

9. The method of the claim 8, wherein said statistical technique is a K-means technique.

10. The method of claim 1, wherein said clustering step is performed by a neural network.

11. The method of claim 10, wherein said neural network is a learning vector quantization neural network.

12. The method of claim 10, wherein said neural network is a self organizing map.

13. The method of claim 1, wherein said normalizing step further comprises the step of detecting and removing outliers in the data.

14. The method of claim 1, further comprising the step of building an analyzer using said working set of data.

15. The method of claim 1, further comprising the step of iteratively selecting data to create one or more analyzers.

16. The method of claim 15, further comprising the step of completing said iterating step when a cutoff level is reached.

17. The method of claim 1, further comprising the step of:

matching each example in a training set to a cluster based on said full set of data;

uniquely associating an analyzer with a cluster;

assigning a recall data to a cluster; and

applying said analyzer associated with said cluster to said recall data to provide an output.

18. An apparatus for generating a working set of data from a full set of data in a computer for building an analyzer, said analyzer having target outputs, said apparatus comprising:

a cumulator for augmenting said full set of data with the target outputs;

an adjuster coupled to said cumulator for normalizing said augmented data;

a clusterizer coupled to said adjuster for clustering said augmented and normalized data; and

a selector coupled to said clusterizer for picking one or more members of said clusterized data as said working set of data.

19. The apparatus of claim 18, wherein said adjuster further comprises a sub-sampler for selecting a subset of said augmented and normalized data.

20. The apparatus of claim 18, wherein said cumulator further comprises a sub-sampler for selecting a portion of said full set of data and a portion of said target outputs.

21. The apparatus of claim 18, further comprising a transformer coupled between said adjuster and said clusterizer for performing univariate, non-linear transformations to said augmented and normalized data.

22. The apparatus of claim 18, wherein said clusterizer uses a weighted distribution.

23. The apparatus of claim 18, wherein said clusterizer uses an equi-probable distribution.

24. The apparatus of claim 18, wherein said outputs are emphasized by said clusterizer.

25. The apparatus of claim 18, wherein said clusterizer uses a statistical technique.

26. The apparatus of the claim 25, wherein said statistical technique is a K-means technique.

27. The apparatus of claim 18, wherein said clusterizer is a neural network.

28. The apparatus of claim 27, wherein said neural network is a learning vector quantization neural network.

29. The apparatus of claim 27, wherein said neural network is a self organizing map.

30. The apparatus of claim 18, wherein said adjuster further comprises:

a non-parametric outlier detector; and

a scaler coupled to said non-parametric outlier detector.

31. The apparatus of claim 18, further comprising an analyzer formed from said working set of data.

32. The apparatus of claim 31, further comprising an analyzer generator, said model generator iteratively picking said working set of data to create one or more of analyzers until an optimal analyzer is found.

33. A program storage device having a computer readable program code embodied therein for generating a working set of data from a full set of data to build an analyzer, said analyzer having target outputs, said program storage device comprising:

a cumulator code for augmenting said full set of data with the target outputs;

an adjuster code coupled to said cumulator code for normalizing said augmented data;

a clusterizer code coupled to said adjuster code for clustering said augmented and normalized data; and

a selector code coupled to said clusterizer code for picking one or more members of said clusterized data as said working set of data.