Comparing the classification performance of multi sensor data fusion based on feature extraction and feature selection
Date
2012-02-27Author
Maz Jamilah, Masnan
Ali Yeon, Md Shakaff, Prof. Dr.
Ammar, Zakaria
Nor Idayu, Mahat
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Show full item recordAbstract
Linear discriminant analysis (LDA) has been widely
used in the classification of multi sensor data fusion. This paper
discusses the performance of LDA when the classifications were
performed based on feature extraction and feature selection
methods. Comparisons were also made based on single sensor
modality. These strategies were studied using a honey dataset
along with two types of sugar concentration collected from two
types of sensors namely electronic nose (e-nose) and electronic
tongue (e-tongue). Assessment of error rate was achieved using
the leave-one-out procedure.