Genetic Algorithm Optimized Feature Transformation - A Comparison with Different Classifiers

When using Genetic Algorithm (GA) to optimize the feature space of pattern classification problems, the performance improved is not only determined by the data set used, but also depend on the classifier. This work compares the improvements acquired by GA optimized feature transformations on several simple classifiers. Some traditional feature transformation techniques, such as Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are also tested to see their effects on the GA optimization. The results based on a real world data and five benchmark data sets from UCI repository show that the improvements after GA optimized feature transformation are in reverse ratio with the original classification rate if the classifier is used alone. Also it is shown that the PCA and LDA transformations on the feature space prior to the GA optimization improved the final result.