Methods for feature selection using classifier ensemble based genetic algorithms

摘要

Methods for performing genetic algorithm-based feature selection are provided herein. In certain embodiments, the methods include steps of applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result; integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result; and outputting the integrated accuracy result to a genetic algorithm as a fitness value for a candidate feature subset, in which genetic algorithm-based feature selection is performed.

主权项

1. A method for performing genetic algorithm-based feature selection, the method comprising:
dividing a set of data samples into at least a first sub-set of data samples and a second sub-set of data samples, wherein the first sub-set of data samples and the second sub-set of data samples include different sub-sets of the set of data samples; applying a data splitting pattern to the first sub-set of data samples, splitting the first sub-set of data samples into a first training set of data samples and a first testing set of data samples, wherein the first training set of data samples and the first testing set of data samples include different data samples of the first sub-set of data samples; creating a first classifier based on the first training set of data samples and the first testing set of data samples; applying one or more different data splitting patterns to the first sub-set of data samples, splitting the first sub-set of data samples into one or more training sets of data samples and one or more testing sets of data samples, wherein the one or more training sets of data samples and the corresponding one or more testing sets of data samples include different data samples of the first sub-set of data samples; creating one or more classifiers based on the one or more training sets of data samples and the corresponding one or more testing sets of data samples; integrating the first classifier and the one or more classifiers, generating an integrated classifier; and outputting the integrated classifier to a genetic algorithm as a fitness value for a candidate feature subset, wherein genetic algorithm-based feature selection is performed using the integrated classifier.