Data Mining
Genetic Algorithms
Creates a data structure containing unencoded and encoded phenotype information.
Decodes an individual’s chromosome into its binary, nominal, integer and real phenotypes.
Encodes an individual’s binary, nominal, integer and real phenotypes into its chromosome.
Adds the individuals in the array individual to an existing population.
Creates a data structure from user supplied phenotypes.
Creates a new population by merging two populations with identical chromosome structures.
Performs the mutation operation on an individual’s chromosome.
Creates a population data structure from user supplied individuals.
Creates a population data structure from randomly generated individuals.
Optimizes a user-defined fitness function using a tailored genetic algorithm.
Naive Bayes
Classifies unknown patterns using a previously trained Naive Bayes classifier.
Trains a Naive Bayes classifier.
Neural Networks
Creates a multilayered feedforward neural network.
Links and modifies a multilayered feedforward neural network.
Initializes weights for multilayered feedforward neural networks prior to network training using one of four user selected methods.
Forecasting Neural Networks
Trains a multilayered feedforward neural network.
Calculates forecasts using trained multilayered feedforward neural networks.
Classification Neural Networks
Trains a multilayered feedforward neural network for classification.
Calculates classifications for trained multilayered feedforward neural networks.
Preprocessing Filters
Scales or unscales continuous data prior to its use in neural network training, testing, or forecasting.
Converts time series data to the format required for processing by a neural network.
Converts time series data sorted within nominal classes in decreasing chronological order to a useful format for processing by a neural network.
Converts nominal data into a series of binary encoded columns for input to a neural network.
Converts ordinal data into proportions.
