System and method for scalable cost-sensitive learning

Details System and method for scalable cost-sensitive learning System and method for scalable cost-sensitive learning

2011-03-08

2011-03-08

Sparks, Donald (Department: 2129)

Data processing: artificial intelligence

Machine learning

Genetic algorithm and genetic programming system

Reexamination Certificate

active

07904397

ABSTRACT:
A method (and structure) for processing an inductive learning model for a dataset of examples, includes dividing the dataset of examples into a plurality of subsets of data and generating, using a processor on a computer, a learning model using examples of a first subset of data of the plurality of subsets of data. The learning model being generated for the first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of the dataset, the ensemble model thereby providing an evolving estimated learning model for the entirety of the dataset if all the subsets were to be processed. The generating of the learning model using data from a subset includes calculating a value for at least one parameter that provides an objective indication of an adequacy of a current stage of the ensemble model.

REFERENCES:
Stolfo et al. ‘JAM: Java agents for meta learning over distributed databases’: 1997, American Association for Artificial Intelligence.
“Progressive Modeling”, Fan, Wang, Yu, Lo, Stofo, 2002, IEEE, 163-179.
“Distributed Data Mining in Credit Card Fraud Detection”, Chan, 1999, IEEE, 1094-7167, pp. 67-74.
“JAM: Java Agents for Meta-Learning over Distributed Databases”; Stofo, American Association for Artificial Intelligence, 1997.

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