Continuous Iterative Guided Spectral Class Rejection Classiﬁcation Algorithm: Part 1
Phillips, Rhonda D.
Watson, Layne T.
Wynne, Randolph H.
MetadataShow full item record
This paper outlines the changes necessary to convert the iterative guided spectral class rejection (IGSCR) classification algorithm to a soft classification algorithm. IGSCR uses a hypothesis test to select clusters to use in classification and iteratively reﬁnes clusters not yet selected for classification. Both steps assume that cluster and class memberships are crisp (either zero or one). In order to make soft cluster and class assignments (between zero and one), a new hypothesis test and iterative reﬁnement technique are introduced that are suitable for soft clusters. The new hypothesis test, called the (class) association signiﬁcance test, is based on the normal distribution, and a proof is supplied to show that the assumption of normality is reasonable. Soft clusters are iteratively reﬁned by creating new clusters using information contained in a targeted soft cluster. Soft cluster evaluation and reﬁnement can then be combined to form a soft classification algorithm, continuous iterative guided spectral class rejection (CIGSCR).