RT Journal Article
T1 PLAHS: 												A Partial Labelling Autonomous Hyper-heuristic System for Industry 4.0 with application on classification of cold stamping process[Formula presented]
A1 Navajas-Guerrero, Adriana
A1 Portillo, Eva
A1 Manjarres, Diana
AB 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.
SN 1568-4946
YR 2023
FD 2023-11
LK https://hdl.handle.net/11556/4124
UL https://hdl.handle.net/11556/4124
LA eng
NO 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
NO Publisher Copyright: © 2023 Elsevier B.V.
NO This work was supported by the Basque Government under the project ref. IT1726-22 .
DS TECNALIA Publications
RD 28 jul 2024