2021 29th Signal Processing and Communications Applications Conference (SIU) 2021
DOI: 10.1109/siu53274.2021.9478012
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The Analysis of Feature Selection with Machine Learning for Indoor Positioning

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Cited by 5 publications
(3 citation statements)
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“…Feature extraction however, projects the original data into a lower dimensional space. In [8], Principal component analysis (PCA) is used to reduce RSS vector size from 520 to 50 with a minor toll on classification accuracy. Accurate localization can be achieved combining two feature extraction methods: Fisher discriminant analysis (FDA) and PCA [17].…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Feature extraction however, projects the original data into a lower dimensional space. In [8], Principal component analysis (PCA) is used to reduce RSS vector size from 520 to 50 with a minor toll on classification accuracy. Accurate localization can be achieved combining two feature extraction methods: Fisher discriminant analysis (FDA) and PCA [17].…”
Section: Related Workmentioning
confidence: 99%
“…Neighbor (KNN) [6], [7] and Support Vector Machine (SVM) [8]. These methods suffer from complexity problem during the online phase.…”
Section: Introductionmentioning
confidence: 99%
“…F eature Selection (FS) has been extensively studied in data mining, [1][2][3] patterns recognition, [4,5] and machine learning. [6,7] Choosing which aspects of a dataset to keep and which to eliminate to create a more useful profile is referred to as "feature selection." The purpose of FS is to preserve the strong properties of the estimation method, so making it more exclusive and, as a result, more effective.…”
Section: Introductionmentioning
confidence: 99%