# Recursive Style Breach Detection with Multifaceted Ensemble Learning

## TL;DR

This paper introduces a supervised, ensemble learning method for detecting and locating style changes in text documents, utilizing recursive analysis to improve accuracy, and achieving top results in a competitive challenge.

## Contribution

It proposes a novel recursive style change detection approach combining diverse classifiers and engineered features, winning the PAN@CLEF 2018 challenge.

## Key findings

- Achieved state-of-the-art performance in style change detection
- Effectively locates precise change positions within texts
- Demonstrates the effectiveness of ensemble and recursive methods

## Abstract

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.