발간년도 : [2021]
| 논문정보 |
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| 논문명(한글) |
[Vol.16, No.1] Feature Selection for Efficient Person Classification of Smartphone Users |
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| 논문투고자 |
Young-In Kim |
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| 논문내용 |
Recently, as sensors that can detect user movements have been built into the smartphone, various research are being conducted to make users conveniently use the smartphone by utilizing them. However, it suffers from computational cost, battery drain, and storage space issues for such continuous processing. In this paper, we propose a feature selection for efficient user classification. In particular, there is a lack of research on how to select and use the optimal features in the machine learning process for user classification. In this paper, we propose a feature selection for efficient user classification. For this, the features of the open HAR dataset in the UCI Machine Learning Repository were used, and experiments were conducted using the open-source software WEKA. In order to select an optimal feature set from the time domain features, five feature subsets were first generated using the five feature selection methods. Next, the three feature subsets were generated by dividing the degree of overlapping of the features in the five previously selected subsets. These sets were tested with the six machine learning classification techniques as inputs. As a result, the accuracy of 93.6%, which is improved than the case of the using the entire features set, was obtained from the feature subset using about 5.9% of the entire features. |
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