US5809490A
Expired / Lapsed
US
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Apparatus and method for selecting a working data set for model development
Assignee: Aspentech Corp
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Filing: 1996-05-03
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Granted: 1998-09-15
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Expires: 2016-05-03
Abstract
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.
Inventors
- 👤 John P. Guiver
- 👤 Casimir C. Klimasauskas
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-28 02:55
CPC Classifications
G; G06; G06N; G06N3/00; G06N3/02; G06N3/08; G06F; G06F18/00; G06F18/20; G06F18/21; G06F18/213; G06F18/2137; G06F18/214; G06F18/217; G06N3/04; G06N3/0499; G06N3/088
Claims Analysis
Total Claims: 36
Claim 1
Independent Claim
69 words
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.
Claim 2
Independent Claim
69 words
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.
Claim 3
Dependent Claim
23 words
The method of claim 1, wherein said clustering step further comprises the step of selecting a subset of said augmented and normalized data.
Claim 4
Dependent Claim
30 words
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.
Claim 5
Dependent Claim
25 words
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.
Claim 6
Dependent Claim
13 words
The method of claim 1, wherein said clustering step uses a weighted distribution.
Claim 7
Dependent Claim
13 words
The method of claim 1, wherein said clustering step uses an equi-probable distribution.
Claim 8
Dependent Claim
17 words
The method of claim 1, wherein said clustering step further comprises the step of emphasizing said outputs.
Claim 9
Dependent Claim
13 words
The method of claim 1, wherein said clustering step uses a statistical technique.
Claim 10
Dependent Claim
14 words
The method of the claim 8, wherein said statistical technique is a K-means technique.
Claim 11
Dependent Claim
15 words
The method of claim 1, wherein said clustering step is performed by a neural network.
Claim 12
Dependent Claim
16 words
The method of claim 10, wherein said neural network is a learning vector quantization neural network.
Claim 13
Dependent Claim
14 words
The method of claim 10, wherein said neural network is a self organizing map.
Claim 14
Dependent Claim
21 words
The method of claim 1, wherein said normalizing step further comprises the step of detecting and removing outliers in the data.
Claim 15
Dependent Claim
19 words
The method of claim 1, further comprising the step of building an analyzer using said working set of data.
Claim 16
Dependent Claim
19 words
The method of claim 1, further comprising the step of iteratively selecting data to create one or more analyzers.
Claim 17
Dependent Claim
20 words
The method of claim 15, further comprising the step of completing said iterating step when a cutoff level is reached.
Claim 18
Dependent Claim
57 words
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.
Claim 19
Independent Claim
90 words
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.
Claim 20
Independent Claim
90 words
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.
Claim 21
Dependent Claim
22 words
The apparatus of claim 18, wherein said adjuster further comprises a sub-sampler for selecting a subset of said augmented and normalized data.
Claim 22
Dependent Claim
29 words
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.
Claim 23
Dependent Claim
27 words
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.
Claim 24
Dependent Claim
12 words
The apparatus of claim 18, wherein said clusterizer uses a weighted distribution.
Claim 25
Dependent Claim
12 words
The apparatus of claim 18, wherein said clusterizer uses an equi-probable distribution.
Claim 26
Dependent Claim
13 words
The apparatus of claim 18, wherein said outputs are emphasized by said clusterizer.
Claim 27
Dependent Claim
12 words
The apparatus of claim 18, wherein said clusterizer uses a statistical technique.
Claim 28
Dependent Claim
14 words
The apparatus of the claim 25, wherein said statistical technique is a K-means technique.
Claim 29
Dependent Claim
12 words
The apparatus of claim 18, wherein said clusterizer is a neural network.
Claim 30
Dependent Claim
16 words
The apparatus of claim 27, wherein said neural network is a learning vector quantization neural network.
Claim 31
Dependent Claim
14 words
The apparatus of claim 27, wherein said neural network is a self organizing map.
Claim 32
Dependent Claim
23 words
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.
Claim 33
Dependent Claim
16 words
The apparatus of claim 18, further comprising an analyzer formed from said working set of data.
Claim 34
Dependent Claim
33 words
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.
Claim 35
Dependent Claim
106 words
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.
Claim 36
Dependent Claim
106 words
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.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2001-09-14 | 2002-03-16 | 2002-09-15 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-09-14 | 2006-03-16 | 2006-09-15 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-09-14 | 2010-03-16 | 2010-09-15 | $7700.00 | Window Closed / Lapsed |