Reducing Misclassification Costs
Elsevier eBooksPublished 1 January 1994
Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk
Citations343
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TL;DR
Algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples are explored and the Reduced Cost Ordering algorithm, a new method for creating a decision list, is described and compared to a variety of inductive learning approaches.
Abstract
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.
Keywords
Computer Science
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