date These problems are best illustrated by the source of our data which come from three er and the Olson Watts   Allison Wilso n Comparison of land cover labels among the three maps shows Henderson Sellers that they agree for only approximately      of the Earth's land surfaces    See Figure    for locations where the three maps were in agreement The problem of using data collected from multiple experts has been documented in other domains as well Smyth For this work based on our expert s suggestions we introduced random error between the following pairs of classes see Table   for the names of the classes  Brodley   Friedl a

## 4.1.2 Credit Approval

The goal of credit approval is to determine whether to give an applicant a credit card Our dataset includes - instances labeled as positive or negative There are nine discrete attributes with two to fourteen values  and six continuous attributes   One or more attribute values are missing from instances The class distribution is fairly well balanced with
" and instances labeled instances labeled This domain was chosen because the choice of whether to give an applicant credit is subjective in nature  Error is introduced because an assessment of future behavior is based on past behavior In an identical application but using a different dataset American Express UK found that loan officers were less than       correct at predicting whether   bor
" and " the This means that of t he labels were in error or the attributes were not adequate to distinguish good from bad ap plicants It is interesting to note that a decision tree was able to classify of borderline applicants correctly in the UK American Express domain  Michie

## 4.1.3 Scene Segmentation

Forthis data set the goal is to learn to segment an image into the seven classes sky cement window brick grass foliage and path Each of the classes has o observations yielding total observations Each instance is the average of a x window of pixels represented by low level  real valued image features  The instances were drawn randomly from a database of seven outdoor images from buildings around the University of Massachusetts at Amherst campus The labels for this dataset were produced by first running the images through a color t segmentation algorithm  the NKGB algorithm  Draper  Collins  Brolio  Hanson   
Riseman and then manually labeling each region on a computer monitor  This procedure produces two types of labeling errors objects that blend into one another such that a region that is predominantly one type of object has pixels from another object class in it and regions for which the boundary is unclear even for visual inspection by humans For example  because sky tends to  poke through" foliage  sky and foliage Draper can be confused in the training data In the expenments that follow we introduced the following confusions sky foliage path grass grass foliage 4