Abstract
AbstractClassification rules using non‐parametric linear discriminant functions are often developed from training sets that are not linearly separable. In these situations it is a common practice among inexperienced workers to use many different pattern recognition methods and then select the results that look the best. However, this practice will only increase the risk of spurious results. To document this, we recently carried out a series of Monte Carlo simulation studies to assess the level of chance classification for two different classification algorithms. The level of chance classification for a given dichotomy is found to vary with the choice of the non‐parametric linear discriminant function employed. Although previous workers have indicated that the degree of separation in the data due to chance is only a function of the object‐to‐descriptor ratio, the results of this study suggest otherwise.
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