Supervised learning programs, such as decision tree learners and neural networks, often must learn Boolean functions. The concept being learned may not easily be expressed in terms of the atomic features given. Constructive induction automatically produces higher level features (combinations of the atomic features), which can improve learning performance. The...
Many machine learning applications require classifiers that minimize an asymmetric loss function rather than the raw misclassification rate. We study methods for modifying C4.5 to incorporate
arbitrary loss matrices. One way to incorporate loss information
into C4.5 is to manipulate the weights assigned to the examples
from different classes. For...
Soil surveys provide essential information for making land use and management decisions on publicly-owned lands in the semi-arid Great Basin. Soil maps produced with conventional mapping techniques are time-consuming, costly, and do not explicitly document the soil scientist's mental soil-landscape model. Predictive soils mapping using decision tree analysis (DTA) can...
Conventional soil maps represent a valuable source of information about soil
characteristics, however they are subjective, very expensive, and time-consuming to
prepare. Also, they do not include explicit information about the conceptual mental
model used in developing them nor information about their accuracy, in addition to the
error associated with...
This study explores the use of predictive mapping techniques in developing Landtype Association (LTA) maps for use in natural resource management. These maps are produced for the USDA Forest Service on a regional basis at a 1:100,000 scale. The goal of this study is to develop and test a method...