Selecting critical features for data classification based on machine learning methods

Abstract Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection is essential. First, to simplify the model by reducing the number of parameters, next to decrease the training time,…

• CU
  Chaoyang University of Technology
• MO
  Ministry of Science and Technology, Taiwan

  Awards: MOST-106-2218-E-324 -002, 107-2221-E-324 -018 -MY2, MOST-107-2221-E-324 -018 -MY2
• TV
  Taichung Veterans General Hospital