%0 Journal Article
%A Navajas-Guerrero, Adriana
%A Portillo, Eva
%A Manjarres, Diana
%T PLAHS: 												A Partial Labelling Autonomous Hyper-heuristic System for Industry 4.0 with application on classification of cold stamping process[Formula presented]
%D 2023
%@ 1568-4946
%U https://hdl.handle.net/11556/4124
%X 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.
%~