Classifier Dependent Dimensionality Reduction for Resource Restricted Environments

High dimensionality problems have become prevalent in present day machine learning applications. The voluminous datasets acquired from sources like cameras, spectroscopes, and other sensors need to be analysed and modelled in a way that uses the available computational resources most efficiently. The paper proposes a genetic algorithm optimised neural network model that takes care of the issue mentioned above. A comparison is also drawn between the results produced by the proposed model and those produced by other contemporary dimensionality reduction algorithms.