Lightweight Comprehensive Evaluation Method for Wireless User Perception Based on Random Forest
Kaixuan Zhang, Guanghui Fan, Jun Zeng, Guan Gui · 2020
Comprehensive evaluation methods of wireless user perception for cellular cells in the same scenario involve multi-indicators. Traditional methods use the weighted sum of all indicators as the evaluation result. However, many unimportant indicators occupy a part of the overall weight, which leads to an unconvincing evaluation result. To achieve a convincing and accurate result, we propose a lightweight comprehensive evaluation method. Firstly, most important indicators are chosen via the random forest algorithm. Secondly, those indicators are weighted via the entropy method. Finally, we compute the score of all cells with the weights. Experiment results are given to show that the cells with higher scores perform better in all indicators, which is coincide with the actual situation. Hence, our proposed method is not only lightweight but also obtain a more accurate result.