2026-06-02T02:30:03Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/82482024-01-09T01:05:28Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Basurto Hornillos, Nuño author Arroyo Puente, Ángel author Cambra Baseca, Carlos author Herrero Cosío, Álvaro author 2022-02 In the field of cybernetic systems and more specifically in robotics, one of the fundamental objectives is the detection of anomalies in order to minimize loss of time. Following this idea, this paper proposes the implementation of a Hybrid Intelligent System in four steps to impute the missing values, by combining clustering and regression techniques, followed by balancing and classification tasks. This system applies regression models to each one of the clusters built on the instances of data set. Subsequently, a variety of balancing techniques are applied to improve the classifier’s ability to discern whether it is in an error or a normal state. These techniques support to obtain better classification ratios in which a robot is close to error and allow us to bring the behavior back to a normal state. The experimentation is performed using a modern and public data set, which has been extracted from a component-based robotic system, in which different anomalies are induced by software in their components. 1367-0751 http://hdl.handle.net/10259/8248 10.1093/jigpal/jzac023 1368-9894 Hybrid Artificial Intelligence System Machine learning Clustering Regression Missing values Component-Based Robot A hybrid machine learning system to impute and classify a component-based robot