Increasing Performance of Multiclass Ensemble Gradient Boost uses Newton-Raphson Parameter in Physical Activity Classifying

The sophistication of smartphones with various sensors they have can be used to recognize human physical activity by placing the smartphone on the human body. Classification of human activities, the best performance is obtained when using machine learning methods, while statistical methods such as logistic regression give poor results. However, the weakness of the logistic regression method in classifying human activities is corrected by using the ensemble technique. This paper proposes to apply the Multiclass Ensemble Gradient Boost technique to improve the performance of the Logistic Regression classification in classifying human activities such as walking, running, climbing stairs, and descending stairs. The results show that the Multiclass Ensemble Gradient Boost Classifier by Estimating the Newton-Raphson Parameter succeeded in improving the performance of logistic regression in terms of accuracy by 29.11%.