Application of Ensemble Methods of Strengthening in Search of Legal Information

Abstract

The article describes problems that cannot always be solved by machine learning algorithms to get an accurate result. For such tasks, you need to use ensembles that actually combine several algorithms in order to simultaneously learn and correct errors, thereby improving the accuracy of results at times, compared to other algorithms. Here are described high accuracy and stability of ensembles, since they are extremely important for fast processing of a large amount of data and at the same time a fairly accurate result. Today, ensembles actively compete with neural networks, since both are quite effective for the same tasks. The work is devoted to the study of ensembles, and more specifically Adaptive Boosting. The main ensemble methods and their advantages and disadvantages were considered. Here are examined the basic principles of the AdaBoost boosting ensemble, analyzes the industries where this ensemble is already used. The study discusses ways for ensembles to work: stacking, running, and boosting. This work analyzes the boosting algorithms such as AdaBoost and gradient boosting. The advantages and disadvantages of these algorithms are presented. This work presents the principle of operation of the AdaBoost algorithm and provides an example of its operation. An analytical review of the AdaBoost algorithm is also given. Examples of using AdaBoost are given. The AdaBoost algorithm was experimentally applied to the data set and its effectiveness was tested. The effectiveness of ensemble boosting methods that compete with neural networks is analyzed. As a result, it is proved that they are somewhat more stable and understandable, which is why they get an advantage in the choice. Also, the AdaBoost algorithm can slightly improve the result compared to “strong” classifiers. Here are presented prospects for applying the AdaBoost algorithm, which is still being developed and will be used in further research.