  We present a supervised approach for style change detection, which aims at
predicting whether there are changes in the style in a given text document, as
well as at finding the exact positions where such changes occur. In particular,
we combine a TF.IDF representation of the document with features specifically
engineered for the task, and we make predictions via an ensemble of diverse
classifiers including SVM, Random Forest, AdaBoost, MLP, and LightGBM. Whenever
the model detects that style change is present, we apply it recursively,
looking to find the specific positions of the change. Our approach powered the
winning system for the PAN@CLEF 2018 task on Style Change Detection.
