Genetic algorithm based feature selection and parameter optimization for support vector regression applied to semantic textual similarity

2015 ◽  
Vol 20 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Bai-hua Su ◽  
Ying-lin Wang
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Support Vector Regression ◽  
Parameter Optimization ◽  
Support Vector ◽  
Semantic Textual Similarity

scholarly journals Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data

2021 ◽  
Vol 54 (1) ◽  
pp. 209-225
Author(s):  
Eugenia Chiarito ◽  
Francesca Cigna ◽  
Giovanni Cuozzo ◽  
Giacomo Fontanelli ◽  
Abraham Mejia Aguilar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Support Vector Regression ◽  
Alpine Grassland ◽  
Support Vector ◽  
Sar Data

scholarly journals Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kuan-Cheng Lin ◽  
Sih-Yang Chen ◽  
Jason C. Hung
Keyword(s):  
Feature Selection ◽  
Parameter Optimization ◽  
Combinatorial Problem ◽  
Combinatorial Problems ◽  
Support Vector ◽  
Local Optimum ◽  
Artificial Fish Swarm Algorithm ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Information And Communication

Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA) employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA) to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

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