scholarly journals Two Step Ant Colony System to Solve the Feature Selection Problem

2006 ◽  
pp. 588-596 ◽  
Author(s):  
Rafael Bello ◽  
Amilkar Puris ◽  
Ann Nowe ◽  
Yailen Martínez ◽  
María M. García
Keyword(s):  
Feature Selection ◽  
Ant Colony ◽  
Selection Problem ◽  
Ant Colony System ◽  
Feature Selection Problem

Multiple parameter control for ant colony optimization applied to feature selection problem

2015 ◽  
Vol 26 (7) ◽  
pp. 1693-1708 ◽  
Author(s):  
Gang Wang ◽  
HaiCheng Eric Chu ◽  
Yuxuan Zhang ◽  
Huiling Chen ◽  
Weitong Hu ◽  
...  
Keyword(s):  
Feature Selection ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Selection Problem ◽  
Parameter Control ◽  
Feature Selection Problem ◽  
Multiple Parameter

scholarly journals The GRA-two algorithm for massive-scale feature selection problem in power system scenario classification and prediction

2021 ◽  
Vol 7 ◽  
pp. 293-303
Author(s):  
Yang Wang ◽  
Xinxiong Jiang ◽  
Faqi Yan ◽  
Yu Cai ◽  
Siyang Liao
Keyword(s):  
Feature Selection ◽  
Power System ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Scale Feature ◽  
Massive Scale

A Heuristic Algorithm for Feature Selection Based on Optimization Techniques

2011 ◽  
pp. 13-26 ◽  
Author(s):  
A. M. Bagirov ◽  
A. M. Rubinov ◽  
J. Yearwood
Keyword(s):  
Feature Selection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Numerical Experiments ◽  
Optimization Techniques ◽  
Selection Problem ◽  
Feature Selection Problem ◽  
Selection Of

The feature selection problem involves the selection of a subset of features that will be sufficient for the determination of structures or clusters in a given dataset and in making predictions. This chapter presents an algorithm for feature selection, which is based on the methods of optimization. To verify the effectiveness of the proposed algorithm we applied it to a number of publicly available real-world databases. The results of numerical experiments are presented and discussed. These results demonstrate that the algorithm performs well on the datasets considered.

scholarly journals GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System

2020 ◽  
Vol 21 (21) ◽  
pp. 7891
Author(s):  
Chi-Wei Chen ◽  
Lan-Ying Huang ◽  
Chia-Feng Liao ◽  
Kai-Po Chang ◽  
Yen-Wei Chu
Keyword(s):  
Feature Selection ◽  
Protein Phosphorylation ◽  
Phosphorylation Site ◽  
Path Selection ◽  
Ant Colony ◽  
Ant Colony System ◽  
Phosphorylation Sites ◽  
Selection Approach ◽  
New Feature ◽  
Feature Selection Approach

Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance.

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