Detection of rare items with TARGET

Abstract

In our new information-based economy, the need to detect a small number of relevant and useful items from a large database arises very often. Standard classifiers such as decision trees and neural networks are often used directly as a detection algorithm. We argue that such an approach is not optimal because these classifiers are almost always built to optimize a criterion that is suitable only for classification but not for detection. For detection of rare items, the misclassification rate and other closely associated criteria are largely irrelevant; what matters is whether the algorithm can rank the few useful items ahead of the rest, something better measured by the area under the ROC curve or the notion of the average precision (AP). We use the genetic algorithm to build decision trees by optimizing the AP directly, and compare the performance of our algorithm with a number of standard tree-based classifiers using both simulated and real data sets.