36 Topics in measurement selection

Publisher Summary One of the major problems one encounters during the design phase of an automatic pattern recognition system is the identification of a good set of measurements. These measurements, to be performed on future unclassified patterns, should enable the recognition system to classify the patterns as correctly as possible. The identification of a measurement set is generally carried out in two phases. The first phase, pattern analysis, uses a variety of techniques that allow the designer to explore raw pattern data, and to infer some of its structure. In the second phase, commonly called feature selection or measurement selection, the preliminary measurement set must be reduced in size to meet the cost/performance trade-off. Various techniques exist to achieve the data reduction called for. One can characterize these techniques either according to the way in which the data reduction was achieved or according to the purpose of the reduced data. This chapter considers the measurement selection techniques of the discriminating category that use the selection method as a means for data reduction.

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