scholarly journals A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems

2021 ◽  
Vol 43 (3) ◽  
pp. B831-B853
Author(s):  
Nicola Demo ◽  
Marco Tezzele ◽  
Gianluigi Rozza
Keyword(s):  
Genetic Algorithm ◽  
Supervised Learning ◽  
Optimization Problems ◽  
High Dimensional ◽  
Learning Approach ◽  
Active Subspaces ◽  
Dimensional Optimization

An Optimized Semi-Supervised Learning Approach for High Dimensional Datasets

2018 ◽  
pp. 294-321
Author(s):  
Nesma Settouti ◽  
Mostafa El Habib Daho ◽  
Mohammed El Amine Bechar ◽  
Mohammed Amine Chikh
Keyword(s):  
Supervised Learning ◽  
High Dimensional ◽  
Diagnostic Process ◽  
Importance Measure ◽  
Learning Approach ◽  
Learning From Data ◽  
Selection Strategies ◽  
Medical Diagnostic ◽  
Small Set ◽  
High Dimensional Datasets

The semi-supervised learning is one of the most interesting fields for research developments in the machine learning domain beyond the scope of supervised learning from data. Medical diagnostic process works mostly in supervised mode, but in reality, we are in the presence of a large amount of unlabeled samples and a small set of labeled examples characterized by thousands of features. This problem is known under the term “the curse of dimensionality”. In this study, we propose, as solution, a new approach in semi-supervised learning that we would call Optim Co-forest. The Optim Co-forest algorithm combines the re-sampling data approach (Bagging Breiman, 1996) with two selection strategies. The first one involves selecting random subset of parameters to construct the ensemble of classifiers following the principle of Co-forest (Li & Zhou, 2007). The second strategy is an extension of the importance measure of Random Forest (RF; Breiman, 2001). Experiments on high dimensional datasets confirm the power of the adopted selection strategies in the scalability of our method.

Improved Embedded Micro-PSO for High-Dimensional Optimization Problems

2012 ◽  
Vol 236-237 ◽  
pp. 1184-1189
Author(s):  
Wen Hua Han ◽  
Chang Dong Zhu
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Small Population ◽  
High Dimensional ◽  
Adaptive Mutation ◽  
Large Scale Optimization ◽  
Continuous Variables ◽  
Scale Optimization ◽  
Dimensional Optimization

This paper presents a novel optimization technique called embedded micro-particle swarm optimization (EMPSO) to solve high-dimensional problems with continuous variables. The proposed EMPSO adopts a population memory which is divided into two portions as the source of diversity, and an external memory to collect particles performing well in an embedded PSO with a very small population size. However, the fact that the new method doesn’t excel in all of the benchmark functions highlights the necessity of developing improvement. Thus an adaptive mutation operator is introduced into EMPSO to remedy the issue. The experimental results show that the improved EMPSO has good performance for solving large-scale optimization problems.

A Novel Modified Tree-Seed Algorithm for High-Dimensional Optimization Problems

2020 ◽  
Vol 29 (2) ◽  
pp. 337-343 ◽  
Author(s):  
Shijie Zhao ◽  
Leifu Gao ◽  
Jun Tu ◽  
Dongmei Yu
Keyword(s):  
Optimization Problems ◽  
High Dimensional ◽  
Dimensional Optimization ◽  
Tree Seed
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