Classification Algorithm Using Branches Importance

摘要

Ensemble methods have attracted a wide attention, as they are learning algorithms that construct a set of classifiers and then classify new data points by taking a weighted vote of their predictions instead of creating one classifier. Random Forest is one of the most popular and powerful ensemble methods, but it suffers from some drawbacks, such as interpretability and time consumption in the prediction phase. In this paper, we introduce a new algorithm branch classification ’BrClssf’ that classifies observations using branches instead of trees, these branches are extracted from a set of randomized trees. The novelty of the proposed method is that it classifies instances according to the branch’s importance, which is defined by some criteria. This algorithm avoids the drawbacks of ensemble methods while remaining efficient. BrClssf was compared to the state-of-the-art algorithms and the results over 15 databases from the UCI Repository and Kaggle show that the BrClssf algorithm gives good performance.