Heuristics for efficient classification

The classification problem is to determine the class of an object when it is costly to observe the values of its attributes. This type of problem arises in fault diagnosis, in the design of interactive expert systems, in reliability analysis of coherent systems, in discriminant analysis of test data, and in many other applications. We introduce a generic decision rule that specifies the next attribute to test at any location in a decision tree. Random searches and tabu searches are applied to determine the best specific form of the rule. The most successful heuristics that we developed are based on the tabu search paradigm. We present computational results for problems with a variety of characteristics and compare our heuristics to an exact dynamic programming algorithm.