Semantic segmentation of mobile LiDAR point clouds is an essential task in many fields such as road network management, mapping, urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. This study presents an approach to improve the evaluation metrics of deep-learning-based point cloud semantic segmentation using 3D geometric features and filter-based feature selection. Information gain (IG), Chi-square (Chi2), and ReliefF algorithms are used to select relevant features. RandLA-Net and Superpoint Grapgh (SPG), the current and effective deep learning networks, were preferred for applying semantic segmentation. RandLA-Net and SPG were fed by adding geometric features in addition to 3D coordinates (x, y, z) directly without any change in the structure of the point clouds. Experiments were carried out on three challenging mobile LiDAR datasets: Toronto3D, SZTAKI-CityMLS, and Paris. As a result of the study, it was demonstrated that the selection of relevant features improved accuracy in all datasets. For RandLA-Net, mean Intersection-over-Union (mIoU) was 70.1% with the features selected with Chi2 in the Toronto3D dataset, 84.1% mIoU was obtained with the features selected with the IG in the SZTAKI-CityMLS dataset, and 55.2% mIoU with the features selected with the IG and ReliefF in the Paris dataset. For SPG, 69.8% mIoU was obtained with Chi2 in the Toronto3D dataset, 77.5% mIoU was obtained with IG in SZTAKI-CityMLS, and 59.0% mIoU was obtained with IG and ReliefF in Paris.