TY - RPRT U1 - Arbeitspapier A1 - Sardá-Espinosa, Alexis A1 - Subbiaha, Subanatarajan A1 - Bartz-Beielstein, Thomas T1 - Conditional Inference Trees for the Knowledge Extraction from Motor Health Condition Data N2 - As the amount of data gathered by monitoring systems increases, using computational tools to analyze it becomes a necessity. Machine learning algorithms can be used in both regression and classification problems, providing useful insights while avoiding the bias and proneness to errors of humans. In this paper, a specific kind of decision tree algorithm, called conditional inference tree, is used to extract relevant knowledge from data that pertains to electrical motors. The model is chosen due to its flexibility, strong statistical foundation, as well as great capabilities to generalize and cope with problems in the data. The obtained knowledge is organized in a structured way and then analyzed in the context of health condition monitoring. The final results illustrate how the approach can be used to gain insight into the system and present the results in an understandable, user-friendly manner T3 - CIplus - 1/2017 KW - Decision tree KW - Conditional inference tree KW - Health condition monitoring KW - Machine learning KW - Knowledge extraction Y1 - 2017 U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-4709 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-4709 SP - 16 S1 - 16 ER -