Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Bezier and B-Spline Techniques
Bezier and B-Spline Techniques
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Hi-index | 0.00 |
Possible changes of the growing season of trees would have significant consequences on forest production. Predicting the onset of tree growth on the basis of climate records can be used for estimating the magnitude of such changes. Conventional methods for estimating the onset of tree growth use cumulative temperature sums. These estimates, however, are quite coarse, and raise questions about making better use of the weather information available. We approach the problem of predicting the onset of tree growth with a predictor based on a combination of a k-nearest neighbor regressor and a linear regressor. The inputs are weighted sums of daily temperatures, where the weights are determined by a subset of Bernstein polynomials chosen with a variable selection methodology. The predictions are smoothed for consecutive days to give more accurate results. We compare our proposed solution to the more conventional approach. The proposed solution is found to be better.