  Measuring research impact and ranking academic achievement are important and
challenging problems. Having an objective picture of research institution is
particularly valuable for students, parents and funding agencies, and also
attracts attention from government and industry. KDD Cup 2016 proposes the
paper acceptance rank prediction task, in which the participants are asked to
rank the importance of institutions based on predicting how many of their
papers will be accepted at the 8 top conferences in computer science. In our
work, we adopt a three-step feature engineering method, including basic
features definition, finding similar conferences to enhance the feature set,
and dimension reduction using PCA. We propose three ranking models and the
ensemble methods for combining such models. Our experiment verifies the
effectiveness of our approach. In KDD Cup 2016, we achieved the overall rank of
the 2nd place.
