  This paper presents details of our winning solutions to the task IV of NIPS
2017 Competition Track entitled Classifying Clinically Actionable Genetic
Mutations. The machine learning task aims to classify genetic mutations based
on text evidence from clinical literature with promising performance. We
develop a novel multi-view machine learning framework with ensemble
classification models to solve the problem. During the Challenge, feature
combinations derived from three views including document view, entity text
view, and entity name view, which complements each other, are comprehensively
explored. As the final solution, we submitted an ensemble of nine basic
gradient boosting models which shows the best performance in the evaluation.
The approach scores 0.5506 and 0.6694 in terms of logarithmic loss on a fixed
split in stage-1 testing phase and 5-fold cross validation respectively, which
also makes us ranked as a top-1 team out of more than 1,300 solutions in NIPS
2017 Competition Track IV.
