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Feature Reduction is one the important aspect in Machine Learning. This method is used to reduce the number of features in a data set while preparing a model. In ML, high-dimensional data refers to data with a large number of features or variables. The curse of dimensionality is one the problem which occurs while preparing a model, where the performance of the model decreases as the number of features increases. This is because the complexity of the model increases with the number of features, and it becomes more difficult to find a better solution. In addition, high-dimensional data can also lead to overfitting, where the model fits the training data too closely and does not generalize well to new data. Traditionally feature reduction can be done in many ways such as principal component analysis (PCA), singular value decomposition (SVD), and linear discriminant analysis (LDA). Each procedure is done in different way But, all these procedures were used to reduce the dimensions and retrieve the information as much possible. Even though there many pre-existing techniques available each technique has its own limitation. In this research, We used genetic algorithm, a bio inspired algorithm which takes inspiration from Darwin's Theory of evolution. The main objective of using this method is to optimize the overall model's computation time. We applied genetic algorithm on a data set for feature reduction. Further, different classifiers are used to test the performance of the resultant dataset. After a set of observations, it is found that genetic algorithms can improve the model's performance with existing dataset and reduces computational time drastically around 50% less time.
Mishra et al. (Fri,) studied this question.