A Noble Feature Selection Method for Human Activity Recognition using Linearly Dependent Concept (LDC)
Win Win Myo, Wiphada Wettayaprasit, Pattara Aiyarak · 2018
Human physical activity recognition process using mobile phones is very complicated with many extracted features in which some features are irrelevant or redundant. Removing irrelevant or redundant features is not only reducing the dataset size but also saving the time consuming task. Hence, a reason to pick out the effective and useful features is our main study. We propose a noble feature selection technique using Linearly Dependent Concept (LDC). Our proposed work attempts a new feature selection method on UCI-HAR dataset. For classification, we use the feed forward neural network and compare the performance result with the original dataset. The goal of our study is not only to find an effective and useful features set from the original dataset but also to be better performance than original dataset. Finally, the experimental result of proposed method gives 2.7% more accuracy and reduces the relative error up to 2.67% of the original dataset.