System, method and computer-accessible medium for making a prediction from market data

A method for generating at least one model, comprising:

• receiving first information related to raw data;
• generating second information by formatting the first information, the second information comprising a plurality of data columns;
• determining a potential informational value of each of the plurality of data columns;
• determining a covariance between each of the plurality of data columns;
• and
• generating third information comprising a feature set based on the determined potential informational value of each of the plurality of data columns and the covariance between each of the plurality of data columns;
• generating fourth information comprising metadata related to the second information;
• and
• using a computer hardware arrangement, generating the at least one model using an islanding procedure based on the second and third information,
• wherein the islanding procedure includes:
• generating, using the computer hardware arrangement, a plurality of islands comprising subset clusters of the second information without regard to a class variable,
• generating a plurality of species comprising groups of evolutionary neural networks using the computer arrangement,
• evaluating the performance of the plurality of species on each of the plurality of generated islands,
• assigning a fraction of the plurality of species to each of the plurality of islands to be species-island pairs based on the performance of the species on each island, and
• continuing an evolution of each of the species-island pairs for a fraction of a number of generations used on a full data set.

A system for generating at least one model, comprising:

• at least one computer hardware arrangement configured to:
• receive first information related to raw data;
• generate second information by formatting the first information, the second information comprising a plurality of data columns;
• determine a potential informational value of each of the plurality of data columns;
• determine a covariance between each of the plurality of data columns;
• and
• generate third information comprising a feature set based on the determined potential informational value of each of the plurality of data columns and the covariance between each of the plurality of data columns;
• generate fourth information comprising metadata related to the second information;
• and
• generate the at least one model using an islanding procedure based on the second and third information,
• wherein the islanding procedure includes:
• generating a plurality of islands comprising subset clusters of the second information without regard to a class variable,
• generating a plurality of species comprising groups of evolutionary neural networks using the computer arrangement,
• evaluating the performance of the plurality of species on each of the plurality of generated islands,
• assigning a fraction of the plurality of species to each of the plurality of islands to be species-island pairs based on the performance of the species on each island, and
• continuing an evolution of each of the species-island pairs for a fraction of a number of generations used on a full data set.