Feature Selection Techniques using for High Dimensional Data in Machine Learning

In machine learning the classification task is normally known as supervised learning. In supervised learning present a specified set of classes and objects are labeled with the correct class. The goal is to generalize from the training objects that will make possible novel objects to be identified as belonging to one of the classes. Evaluating the performance of learning algorithms is a fundamental aspect of machine learning. The main objective of this paper is to study the classification accuracy using feature selection with machine learning algorithms. The dimensionality of the data is reduced by implementing Feature selection and accuracy of a learning algorithm improved. The filtered feature space that is, the condensed feature space provided by a filter. The advantage of feature selection for learning can include a reduction in the amount of data required to achieve learning, improved predictive accuracy, learned knowledge that is more compact and easily comprehend, and reduced execution time. 1