Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Classification of meteorological volumetric radar data using rough set methods
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
A New Version of Rough Set Exploration System
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Rough set uncertainty for robotic systems
Journal of Computing Sciences in Colleges
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
A rough set approach for traffic light system with no fixed cycle
Journal of Computing Sciences in Colleges
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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The basic contribution of this paper is the presentation of two methods that can be used to design a practical robot obstacle classification system based on data mining methods from rough set theory. These methods incorporate recent advances in rough set theory related to coping with the uncertainty in making obstacle classification decisions either during the operation of a mobile robot. Obstacle classification is based on the evaluation of data acquired by proximity sensors connected to a line-crawling robot useful in inspecting power transmission lines. A fairly large proximity sensor data set has been used as means of benchmarking the proposed classification methods, and also to facilitate comparison with other published studies of the same data set. Using 10-fold cross validated paired t-test, this paper compares the rough set classification learning method with the Waikato Environment for Knowledge Analysis (WEKA) classification learning method.