Review on Feature Selection for the Analysis of Human Activities and Postural Transitions on Smart Phone
Keywords:
Recursive feature elimination, feature selection, irrelevant, redundant, prediction, accuracy.Abstract
Most of the data in real world used for prediction have many features which are relevant and irrelevant. While performing prediction with large number of features, it will depreciate the performance in the terms of accuracy, space and time. To address this, features which influence the target prediction has to considered. Features which are irrelevant and redundant has to be eliminated. For the purpose, there are many algorithms. For high dimensional data like smartphone based recognition of human activities and postural transitions, requires feature selection. Many feature selection methods are applied and compared to get the best performance in terms of accuracy. It is found that Recursive feature elimination outperform others.
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