Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression
Reexamination Certificate
2006-03-28
2006-03-28
Phan, Thai (Department: 2128)
Data processing: structural design, modeling, simulation, and em
Modeling by mathematical expression
C703S006000, C706S012000, C706S014000, C706S020000
Reexamination Certificate
active
07020593
ABSTRACT:
A new method is used to model the class probability from data that is based on a novel multiplicative adjustment of the class probability by a plurality of items of evidence induced from training data. The optimal adjustment factors from each item of evidence can be determined by several techniques, a preferred embodiment thereof being the method of maximum likelihood. The evidence induced from the data can be any function of the feature variables, the simplest of which are the individual feature variables themselves. The adjustment factor of an item of evidence Ejis given by the ratio of the conditional probability P(C|Ej) of the class C given Ejto the prior class probability P(C), exponentiated by a parameter aj. The method provides a new and useful way to aggregate probabilistic evidence so that the final model output exhibits a low error rate for classification, and also gives a superior lift curve when distinguishing between any one class and the remaining classes. A good prediction for the class response probability has many uses in data mining applications, such as using the probability to compute expected values of any function associated with the response, and in many marketing applications where lift curves are generated for selected prioritized target customers.
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Hong Se June
Hosking Jonathan R.
Natarajan Ramesh
International Business Machines - Corporation
Kaufman Stephen C.
Phan Thai
Whitham Curtis & Christofferson, P.C.
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