Theses and Dissertations at Montana State University (MSU)
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Item Utilizing distributions of variable influence for feature selection in hyperspectral images(Montana State University - Bozeman, College of Engineering, 2019) Walton, Neil Stewart; Chairperson, Graduate Committee: John SheppardOptical sensing has been applied as an important tool in many different domains. Specifically, hyperspectral imaging has enjoyed success in a variety of tasks ranging from plant species classification to ripeness evaluation in produce. Although effective, hyperspectral imaging can be prohibitively expensive to deploy at scale. In the first half of this thesis, we develop a method to assist in designing a low-cost multispectral imager for produce monitoring by using a genetic algorithm (GA) that simultaneously selects a subset of informative wavelengths and identifies effective filter bandwidths for such an imager. Instead of selecting the single fittest member of the final population as our solution, we fit a univariate Gaussian mixture model to a histogram of the overall GA population, selecting the wavelengths associated with the peaks of the distributions as our solution. By evaluating the entire population, rather than a single solution, we are also able to specify filter bandwidths by calculating the standard deviations of the Gaussian distributions and computing the full-width at half-maximum values. In our experiments, we find that this novel histogram-based method for feature selection is effective when compared to both the standard GA and partial least squares discriminant analysis. In the second half of this thesis, we investigate how common feature selection frameworks such as feature ranking, forward selection, and backward elimination break down when faced with the multicollinearity present in hyperspectral data. We then propose two novel algorithms, Variable Importance for Distribution-based Feature Selection (VI-DFS) and Layer-wise Relevance Propagation for Distribution-based Feature Selection (LRP-DFS), that make use of variable importance and feature relevance, respectively. Both methods operate by fitting Gaussian mixture models to the plots of their respective scores over the input wavelengths and select the wavelengths associated with the peaks of each Gaussian component. In our experiments, we find that both novel methods outperform variable ranking, forward selection, and backward elimination and are competitive with the genetic algorithm over all datasets considered.Item Impacts of copula modeling and parametric variation on revenue policy premium rates in multiple peril crop insurance(Montana State University - Bozeman, College of Agriculture, 2015) Simonds, Seth Neil; Chairperson, Graduate Committee: Vincent H. Smith; Joseph Atwood (co-chair)Federally subsidized multiple peril crop insurance is the primary mechanism by which U.S. farmers receive public income. This study investigates the role of copula modeling in developing revenue product premium rates for multiple peril crop insurance. Simulation and empirical experiments are used to examine the viability of a ratemaking practice that relies on an assumed Normal copula. This study shows that the assumption of a copula cannot be statistically justified and that premium rates generated within copulas and between alternative copulas can diverge as a function of the marginal price and yield distributions, their relationship and the level of protection a producer elects. The current ratemaking practice does not account for the imprecision of premium rates implicit to a copula based approach. A copula selection method is proposed and examined in order to reduce premium rate imprecision resulting from copula misspecification. A non-copula based ratemaking method may better meet the overt policy objectives of multiple peril crop insurance.Item Demonstration of normalized differential detection using smart pixels with smart illumination(Montana State University - Bozeman, College of Letters & Science, 2000) Chen, Xiaofang