A novel technique to detect a suboptimal threshold of neighborhood rough sets for hyperspectral band selection
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Neighborhood rough sets (NRS), an extension of rough sets, are widely used for feature selection. Although NRS have the advantage of dealing with the continuous data, success of the NRS-based feature selection techniques is strongly dependent on a predefined threshold value which determines the size of neighborhood granule. In this paper, we have proposed a novel technique to detect a suitable threshold of NRS for hyperspectral band selection. Our proposed technique analyzes the changes in boundary regions to select a suitable threshold value that keeps less uncertain boundary samples into positive region and more uncertain boundary samples into boundary region of the decision attribute. The effectiveness of the proposed technique is assessed by using different data sets.
KeywordsFeature selection Hyperspectral images Neighborhood rough sets Support vector machine
The first named author is supported by University Grants Commission, New Delhi (Grant No. F./2015-16/NFO-2015-17-OBC-ASS-46084).
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Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
Consent to submit has been received explicitly from all co-authors, as well as from the responsible authorities tacitly or explicitly at the institute/organization where the work has been carried out before the work is submitted.
Research involving human participants and/or animals
Our research does not include human participants or animals.
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