lFIT: an unsupervised discretization method based on the Ramer–Douglas–Peucker algorithm

Abstract

Discretization is the process of converting continuous values into discrete values. It is a preprocessing step of several machine learning and data mining algorithms and the quality of discretization may drastically affect the performance of these algorithms. In this study we propose a discretization algorithm, namely line fitting-based discretization (lFIT), based on the Ramer-Douglas-Peucker algorithm. It is a static, univariate, unsupervised, splittingbased, global, and incremental discretization method where intervals are determined based on the Ramer-Douglas-Peucker algorithm and the quality of partitioning is assessed based on the standard error of the estimate. To evaluate the performance of the proposed method, a set of experiments are conducted on ten benchmark datasets and the achieved results are compared to those obtained by eight state-of-the-art discretization methods. Experimental results show that lFIT achieves higher predictive accuracy and produces less number of inconsistency while it generates larger number of intervals. The obtained results are also validated through Friedman's test and Holm's post hoc test which revealed the fact that lFIT produces discretization schemes that statistically comply both with supervised and unsupervised discretization methods.

Expert system design for vacant parking space location using automatic learning and artificial vision

“…7a in more detail, you will see that even when using the polygon approximation method, which generates the least number of points possible, the lines are not completely straight and have small variations that cause each line to be defined internally by many points. Therefore, to reduce the number of points and find the simplest representation of the parking lot to define the coordinates of the spaces, a polygon approximation is implemented using the Ramer-Douglas-Peucker algorithm [ 30 ].…”

Hierarchical Collision Avoidance Algorithm with Route Exchange Concept for Autonomous and Conventional Ships

“…Finally, the simplified trajectory segment is merged with the initial plan to be exchanged with the target ships. Some spatial and temporal precision is lost during downsampling to generate the occupancy grid map, scaling ship speeds and using straight-line prediction for target ships' Algorithm 2 The Ramer-Douglas-Peucker algorithm [48] 1: function RDP(P , ε) return P ′ future positions, and finally simplifying the resulting trajectory with the Ramer-Douglas-Pecker algorithm. Therefore, a proactive collision avoidance algorithm based on the MPC method is implemented to further optimize the A* generated trajectories.…”