A Boosted Review for Decision Tree Boosting
Published in Unofficial Publication, 2018
Abstract
Shallow decision trees are weak classifiers that can be combined to create a robust predictive model. Ensemble methods have benefits over a simple decision tree like reducing bias, over-fitting and accuracy improvement. This study has the purpose to study the state-of-art of boosting trees techniques and its applications and analyse qualitatively what has been published on boosting methods. We have searched on IEEE, Scopus, ACM and Elsevier repositories with a string derived from technique PICO. We found a total of 102 papers, and after applying our criteria we got 47 papers. We summarized the methods of gradient boosting decision trees for classification and regression problems. We analysed the algorithms XGBoost, LightGBM, CatBoost, LambdaMART in different published scenarios. We have classified the found papers in the subcategories: business, new method, civil engineering, network security, health, model improvement. We conclude that boosted tree-based algorithms is a field of research in exploration, and development of new techniques.