System and method for scalable cost-sensitive learning

An apparatus, comprising:

• a database divider, as executed by a processor of said apparatus, for dividing a dataset of example data into N subsets of data;
• a base classifier calculator, as executed by said processor, for developing a learning model for example data in a subset of said N subsets;
• an ensemble calculator, as executed by said processor, for progressively developing an evolving aggregate learning model (ensemble model) by successively integrating learning models from successive subsets of said N subsets, said ensemble calculator further calculating at least one parameter that provides an objective indication of an adequacy of a current stage of said ensemble model;
• a memory interface, as executed by said processor, to retrieve data from a memory and to store data as said ensemble model is progressively developed;
• and
• a graphical user interface (GUI), as executed by said processor, to permit user inputs and to display information to said user.

A storage medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method of processing an inductive learning model for a dataset of examples, said method comprising:

• dividing said 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 said plurality of subsets of data, wherein:
• said learning model being generated for said first subset comprises an initial stage of an evolving aggregate learning model (ensemble model) for an entirety of said dataset, said ensemble model thereby providing an evolving estimated learning model for the entirety of said dataset if all said subsets were to be processed, and
• said generating said 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 said ensemble model.