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A novel neighbor selection approach for KNN: a physiological status prediction case study
| Content Provider | ACM Digital Library |
|---|---|
| Author | Kaveh-Yazdy, Fatemeh Zare-Mirakabad, Mohammad-Reza Xia, Feng |
| Abstract | Conventional weighting KNNs enhance the accuracy of label selection by weighting the neighbors. In this paper, however, we propose a novel weighting approach which weights the distances to find the neighbors more accurately. We take importance of size of classes and dispersity of samples into account for this purpose. Moreover we use LDA that saves discrimination level of data for reducing dimension of problem space. We show the effectivity of our proposed method on PDMC-04 dataset to predict physiological status of human subjects. |
| Starting Page | 1 |
| Ending Page | 7 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450315531 |
| DOI | 10.1145/2346604.2346607 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-08-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Weighted k nearest neighbor Physiological status Linear discriminant analysis Class dispersity |
| Content Type | Text |
| Resource Type | Article |
