  Classification is a common statistical task in many areas. In order to
ameliorate the performance of the existing methods, there are always some new
classification procedures proposed. These procedures, especially those raised
in the machine learning and data-mining literature, are usually complicated,
and therefore extra effort is required to understand them and the impacts of
individual variables in these procedures. However, in some applications, for
example, pharmaceutical and medical related research, future developments
and/or research plans will rely on the interpretation of the classification
rule, such as the role of individual variables in a diagnostic rule/model.
Hence, in these kinds of research, despite the optimal performance of the
complicated models, the model with the balanced ease of interpretability and
satisfactory performance is preferred. The complication of a classification
rule might diminish its advantage in performance and become an obstacle to be
used in those applications. In this paper, we study how to improve the
classification performance, in terms of area under the receiver operating
characteristic curve of a conventional logistic model, while retaining its ease
of interpretation. The proposed method increases the sensitivity at the whole
range of specificity and hence is especially useful when the performance in the
high-specificity range of a receiver operating characteristic curve is of
interest. Theoretical justification is presented, and numerical results using
both simulated data and two real data sets are reported.
