Label ranking aims to map instances to an order over a predefined set of labels. It is ideal that the label ranking model is trained by directly maximizing performance measures on training data. However, existing studies on label ranking models mainly based on the minimization of classification errors or rank losses. To fill in this gap in label ranking, in this paper a novel label ranking mode...

One approach to option ranking in qualitative decision making is first to automatically construct a quantitative evaluation model using the qualitative and quantitative (QQ) method and then use the quantitative model for ranking. However, the quantitative model constructed by QQ, which uses linear techniques, fails to provide consistent and complete option ranking of non-monotone decision prefe...

Research interest often lies in comparing structural model specifications implying different relationships among latent factors. In this context parceling is commonly accepted, assuming the item-level measurement structure is well known and, conservatively, assuming items are unidimensional in the population. Under these assumptions, researchers compare competing structural models, each specifi...

We visited Dan Peterson at Wilson Lab on Tuesday, April 30 and had a very fruitful 5hour discussion with him on all aspects of drift-chamber construction and operation. Some of what we learned is summarized below. A supporting packet of xeroxes from the CLEO III TDR and the drift chamber review of 11/95 is available. Figure 1: Half section of the CLEO III drift chamber, DR3.

With university rankings gaining both in popularity and influence, university administrators develop strategies to improve their rankings. To better understand this competition for ranking, we present an adjacent category logit model to address the localized nature of ranking competition and include lagged rank as an independent variable to account for stickiness of ranking. Calibrating our mod...