The attribute hierarchy method (AHM) (Leighton, Gierl, & Hunka, 2004), which is based on the assumption that test items can be described by a set of hierarchically ordered attributes, is designed to estimate examinees’ level of competency as well as their profiles for mastering a set of attributes. The AHM, by incorporating the assumption of attribute dependency, brings an important cognitive p...

Attribute subsetting is a meta-classification technique, based on learning multiple base-level classifiers on projections of the training data. In prior work with nearest-neighbour base classifiers, attribute subsetting was modified to learn only one classifier, then to selectively ignore attributes at classification time to generate multiple predictions. In this paper, the approach is generali...

Weight determination aims to determine the importance of different attributes; determining accurate weights can significantly improve accuracy classification and clustering. This paper proposes an method for attribute weight determination. The uses distance from sample point each class center point. It minimize determines constraints through objective function. In this paper, obtained by exact ...