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  1. University of Information Technology
  2. Faculty of Computing

A Noble Feature Selection Method for Human Activity Recognition using Linearly Dependent Concept (LDC)

http://hdl.handle.net/20.500.12678/0000005865
http://hdl.handle.net/20.500.12678/0000005865
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666f6fbb-2458-4d6f-a133-9ebf4e4b50a2
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A A Noble Feature Selection Method for Human Activity Recognition using Linearly Dependent Concept (LDC).pdf (481 Kb)
© 2018 Association for Computing Machinery.
Publication type
Conference paper
Upload type
Publication
Title
Title A Noble Feature Selection Method for Human Activity Recognition using Linearly Dependent Concept (LDC)
Language en
Publication date 2018-02-01
Authors
Win Win Myo
Wiphada Wettayaprasit
Pattara Aiyarak
Description
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.
Keywords
Neural Network, Linearly Dependent, Feature Selection, Mobile Phone, Sensor, Human Activity Recognition
Identifier 10.1145/3185089.3185131
Conference papers
ICSCA
February, 2018
7th International Journal on Software and Computer Applications
Kuantan, Malaysia
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