Patent EP0625861A2 successfully refreshed from Google Patents.
Patent US20030176931A1 successfully refreshed from Google Patents.
US20030176931A1
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US
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Method for constructing segmentation-based predictive models
Assignee: GlobalFoundries Inc
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Filing: 2002-03-11
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Granted: —
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Expires: 2022-03-11
Abstract
Abstract The present invention generally relates to computer databases and, more particularly, to data mining and knowledge discovery. The invention specifically relates to a method for constructing segmentation-based predictive models, such as decision-tree classifiers, wherein data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment. The present invention contemplates a computerized method for automatically building segmentation-based predictive models that substantially improves upon the modeling capabilities of decision trees and related technologies, and that automatically produces models that are competitive with, if not better than, those produced by data analysts and applied statisticians using traditional, labor-intensive statistical techniques. The invention achieves these properties by performing segmentation and multivariate statistical modeling within each segment simultaneously. Segments are constructed so as to maximize the accuracies of the predictive models within each segment. Simultaneously, the multivariate statistical models within each segment are refined so as to maximize their respective predictive accuracies.
Inventors
- 👤 Edwin Pednault
- 👤 Ramesh Natarajan
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 13:53
CPC Classifications
G; G06; G06F; G06F16/00; G06F16/20; G06F16/24; G06F16/245; G06F16/2458; G06F16/2465; G06F18/00; G06F18/20; G06F18/21; G06F18/214; G06F18/23; G06F18/231; G06F18/24; G06F18/243; G06F18/24323; G06N; G06N20/00; G06N5/00; G06N5/01; G06N7/00; G06N7/01; G06V; G06V10/00; G06V10/70; G06V10/762; G06V10/7625; G06V10/764; G06V10/77; G06V10/774; Y; Y10; Y10S; Y10S707/00; Y10S707/99941; Y10S707/99942
Claims Analysis
Total Claims: 4
Claim 1
Independent Claim
222 words
1 . A process performed on a computer for constructing segmentation-based predictive models, the method steps comprising: 1) accessing a collection of training data records comprising examples of input values that are available to the segmentation-based predictive model together with corresponding desired output value(s) that the segmentation-based predictive model is intended to predict; 2) generating a plurality of data segments defined by tests on some, all, or none of the available inputs, and generating one or more segment models for each generated data segment, the generation method comprising the steps of: a) for at least one generated data segment in the final plurality, generating a plurality of candidate data segments and associated segments models wherein at least one segment model for at least one candidate data segment is a multivariate segment model, and b) selecting from among the plurality of candidate data segments for that generated data segment a best candidate data segment that optimizes a degree of fit measure based on one or more of the multivariate statistical models generated for the selected candidate data segment; and 3) pruning the plurality of generated data segments and associated segment models by selecting a subset of generated data segments, together with one generated segment model for each data segment selected, so as to optimize the predictive accuracy of the resulting segmentation-based predictive model.
Claim 2
Dependent Claim
222 words
1 . A process performed on a computer for constructing segmentation-based predictive models, the method steps comprising: 1) accessing a collection of training data records comprising examples of input values that are available to the segmentation-based predictive model together with corresponding desired output value(s) that the segmentation-based predictive model is intended to predict; 2) generating a plurality of data segments defined by tests on some, all, or none of the available inputs, and generating one or more segment models for each generated data segment, the generation method comprising the steps of: a) for at least one generated data segment in the final plurality, generating a plurality of candidate data segments and associated segments models wherein at least one segment model for at least one candidate data segment is a multivariate segment model, and b) selecting from among the plurality of candidate data segments for that generated data segment a best candidate data segment that optimizes a degree of fit measure based on one or more of the multivariate statistical models generated for the selected candidate data segment; and 3) pruning the plurality of generated data segments and associated segment models by selecting a subset of generated data segments, together with one generated segment model for each data segment selected, so as to optimize the predictive accuracy of the resulting segmentation-based predictive model.
Claim 3
Dependent Claim
101 words
2 . A process performed on a computer for training multivariate segment model objects, the method steps comprising: 1) accessing a collection of training data records comprising examples of input values that are available to the multivariate segment model object together with corresponding desired output value(s) that the model is intended to predict; 2) presenting the training data records to a multivariate segment model object by calling one or more scan-data-record interface functions; and 3) repeating method steps 1 and 2 until the multivariate segment model object indicates that it does not need to have the training records presented over again.
Claim 4
Dependent Claim
101 words
2 . A process performed on a computer for training multivariate segment model objects, the method steps comprising: 1) accessing a collection of training data records comprising examples of input values that are available to the multivariate segment model object together with corresponding desired output value(s) that the model is intended to predict; 2) presenting the training data records to a multivariate segment model object by calling one or more scan-data-record interface functions; and 3) repeating method steps 1 and 2 until the multivariate segment model object indicates that it does not need to have the training records presented over again.
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