PLAHS: A Partial Labelling Autonomous Hyper-heuristic System for Industry 4.0 with application on classification of cold stamping process[Formula presented]
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2023-11
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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.
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Publisher Copyright: © 2023 Elsevier B.V.
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Navajas-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.2023.110718