A particle swarm optimization-based feature selection for unsupervised transfer learning

On the use of evolutionary and swarm intelligence algorithms in transfer learning approaches: a review

“…In the Classification problems, the objective is to assign a category or label to a given object based on its observed characteristics. 38 Assigned to this problem are the algorithms SemPSO, 27 UnPSO, 27 FSUTL-PSO, 28 SBPSO, 29 and COMB-PSO-TL. 30 These algorithms are applied to image and document classification using datasets such as Office+Caltech, 39 PIE Face Recognition, 40 Gas Sensor, 41 Handwritten Digits, 42,43 Prostate, 44 and TripAdvisor.…”

“…45 For Regression problems, the goal is predicting or estimating a continuous value based on observed characteristics. 28 The primary studies analyzed involve Symbolic Regression, which aims to discover a symbolic expression capturing the underlying structure of the data, providing interpretability to the model. This is particularly useful for missing values, a problem explored by the three Genetic Programming-based algorithms (MTGPTL, 31 MTGPDA, 32 and MTGP-Based TL 33 ).…”

“…Particle Swarm Optimization Nguyen et al 27 Sanodiya et al 28 Castillo-García et al 29 Dhrif et al 30 Feature Transformation Based Domain Adaptation Genetic Programming Al-Helali et al 31 Al-Helal et al 32 Al-Helali et al 33 Parameter-Selection Particle Swarm Optimization Sanodiya et al 34 The most common TL techniques among the primary studies collected for this review are "Instance Weighting," "Feature Selection-Based Domain Adaptation," "Feature Transformation-Based Domain Adaptation," and "Parameter Selection".…”

“…In Feature Selection-Based Domain Adaptation, the expectation is that datasets derived from two original sets, when projected into the new feature space, exhibit greater proximity than the original sets. 27 Among the base studies, five algorithms are identified: Semi-Supervised Particle Swarm Optimization (SemPSO), 27 Unsupervised Particle Swarm Optimization (UnPSO), 27 Feature Selection-Based Transfer Learning Approach Using Particle Swarm Optimization for Unsupervised Transfer Learning (FSUTL-PSO), 28 Sticky Binary Particle Swarm Optimization (SBPSO), and Feature Selection Algorithm for Transfer Learning (COMB-PSO-TL). 30 In addition to feature-based adaptation, there is Feature Transformation-Based Domain Adaptation, or Feature Weighting.…”

“…In the Feature Selection-Based Transfer Learning Approach Using Particle Swarm Optimization for Unsupervised Transfer Learning (FSUTL-PSO) 28 algorithm, the nature-inspired algorithm aims to select a good subset of features between the source and target domains to eliminate the risk of degenerated feature transformation. PSO constructs a common feature space using features with high invariant characteristics.…”