METHOD FOR CONSTRUCTING SEGMENTATION-BASED PREDICTIVE MODELS

• US 20090030864A1
• Filed: 09/04/2008
• Published: 01/29/2009
• Est. Priority Date: 03/11/2002
• Status: Active Grant

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Pednault, Edwin Peter Dawson, Natarajan, Ramesh
• Granted Patent: US 8,392,153 B2
• Time in Patent Office: Days
• US Class Current: 706/45
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/231 Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram
• CPC Class Codes: G06F 18/24323 Tree-organised classifiers
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 10/7625 Hierarchical techniques, i.e. dividing or merging patterns to obtain a tree-like representation; Dendograms
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 10/774 Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation

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.

Claims

1. A method for a process performed on a computer for training multivariate segment model objects, the method comprising:

1) accessing a collection of training data records comprising examples of input values that are available to a multivariate segment model object, together with corresponding desired output value(s) that the multivariate segment model is intended to predict;

2) presenting the training data records to the multivariate segment model object by calling one or more scan-data-record interface functions, wherein the segment model object responds by generating and pruning pluralities of data segments and associated segment models, at least one of which comprises a multivariate segment model; and

3) repeating said accessing and said presenting until the multivariate segment model object indicates that it does not need to have the training records presented over again.

2. A signal-bearing storage medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform the method of claim 1.

3. A digital processing apparatus configured with a program of machine-readable instructions to perform the method of claim 1.