Abstract: | Let us consider m general populations π1, …,πm. Each object belonging to these populations is represented by (p ± 1) characteristics x1, x2,…,xp,y. A certain object, which is an element of one of the m general populations π1,…,πm has to be classified into the correct population. It will be assumed that knowledge of the value of the characteristic y would permit its correct classification, but that the observation of this characteristic is expensive, difficult or dangerous, as e.g. in medical applications. y is correlated with a set of p characteristics x1,x2,…,xp, which are observed sequentially. The classification procedure is based on the division of the space of the observed value of characteristics x1,x2,…,xp into nonintersecting areas determined so as to minimize the value of BAYES' risk given by equation (3). |