Reducing Misclassification Costs

We explore algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples. First, we consider inductive learning of classification rules. The Reduced Cost Ordering algorithm, a new method for creating a decision list (i.e., an ordered set of rules) is described and compared to a variety of inductive learning approaches. Next, we describe approaches that attempt to minimize costs while avoiding overfitting, and introduce the Clause Prefix method for pruning decision lists. Finally, we consider reducing misclassification costs when a prior domain theory is available.

• Type: Conference
• Year: 1994
• Where: ICML
• Authors: Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk
• Keywords: Decision Lists, ICML 1994, Inductive Learning, Inductive Learning Approaches, Machine Learning