Navajas-Guerrero, AdrianaPortillo, EvaManjarres, Diana2024-07-242024-07-242023-11Navajas-Guerrero , A , Portillo , E & Manjarres , D 2023 , ' PLAHS : A Partial Labelling Autonomous Hyper-heuristic System for Industry 4.0 with application on classification of cold stamping process[Formula presented] ' , Applied Soft Computing Journal , vol. 147 , 110718 . https://doi.org/10.1016/j.asoc.2023.1107181568-4946https://hdl.handle.net/11556/4124Publisher Copyright: © 2023 Elsevier B.V.In real-life industry it is difficult to have fully-labelled datasets due to lack of time, resources or knowledge. In this sense, this paper proposes the design and development of a Partial Labelling Autonomous Hyper-heuristic System PLAHS, a solution that autonomously labels partially labelled databases and evaluates the yielded labelling solution by means of a novel Trustworthiness Metric (TM). The proposal combines a hyper-heuristic inspired approach with a Semi Supervised Learning Clustering (SSLC) methodology that optimizes the parameters of different clustering algorithms, based on a novel semi-supervised metric named Partially Supervised Optimization Metric (PSOM). The proposal has been tested with promising and excellent results on both a real use case for labelling work orders in a cold stamping press, and 13 databases from the UCI (multivariate data) and UCR (time series data) repositories.enginfo:eu-repo/semantics/restrictedAccessjournal article10.1016/j.asoc.2023.110718Harmony searchHyper-heuristicIndustry 4.0Partial labellingSemi-supervised clustering metricTrustworthiness metricSoftwareSDG 9 - Industry, Innovation, and Infrastructurehttp://www.scopus.com/inward/record.url?scp=85169918840&partnerID=8YFLogxK