FEATURE-SELECTION USING EXPECTED ATTAINABLE DISCRIMINATION

We propose expected attainable discrimination (EAD) as a measure to se lect discrete valued features for reliable discrimination between two classes of data. EAD is an average of the area under the ROC curves ob tained when a simple histogram probability density model is trained an d tested on many random partitions of a data set, EAD can be incorpora ted into various stepwise search methods to determine promising subset s of features, particularly when misclassification costs are difficult or impossible to specify. Experimental application to the problem of risk prediction in pregnancy is described. (C) 1998 Elsevier Science B ,V, All rights reserved.