scholarly journals Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization

2019 ◽  
Vol 27 (1) ◽  
pp. 171-183
Author(s):  
Ali Hakem Jabor ◽  
Ali Hussein Ali
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Selection Method ◽  
Binary Particle Swarm Optimization ◽  
Features Selection ◽  
Nearest Neighbour ◽  
Selection Algorithm ◽  
Swarm Optimization

The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swarm Optimization (DHFS) test, and compared with 26 well-known datasets of UCI machine learning. The numeric experiments result imply that the DHFS better performance compared with full features and that selected by the mentioned algorithms (Genetic Algorithm and Binary Particle Swarm Optimization). 

A Unified Feature Selection Model for High Dimensional Clinical Data Using Mutated Binary Particle Swarm Optimization and Genetic Algorithm

2018 ◽  
Vol 13 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Thendral Puyalnithi ◽  
Madhuviswanatham Vankadara
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Clinical Data ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Data Sets ◽  
Data Set ◽  
Swarm Optimization ◽  
Binary Genetic Algorithm

This article contends that feature selection is an important pre-processing step in case the data set is huge in size with many features. Once there are many features, then the probability of existence of noisy features is high which might bring down the efficiency of classifiers created out of that. Since the clinical data sets naturally having very large number of features, the necessity of reducing the features is imminent to get good classifier accuracy. Nowadays, there has been an increase in the use of evolutionary algorithms in optimization in feature selection methods due to the high success rate. A hybrid algorithm which uses a modified binary particle swarm optimization called mutated binary particle swarm optimization and binary genetic algorithm is proposed in this article which enhanced the exploration and exploitation capability and it has been a verified with proposed parameter called trade off factor through which the proposed method is compared with other methods and the result shows the improved efficiency of the proposed method over other methods.

scholarly journals A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Naeimeh Elkhani ◽  
Ravie Chandren Muniyandi
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Membrane Computing ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Binary Particle Swarm Optimization ◽  
P System ◽  
Processing Unit ◽  
Swarm Optimization

Membrane computing is a theoretical model of computation inspired by the structure and functioning of cells. Membrane computing models naturally have parallel structure, and this fact is generally for all variants of membrane computing like kernel P system. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in membrane computing. This paper uses multiple cores processing tools in MATLAB as a parallel tool to implement proposed feature selection method based on kernel P system-multiobjective binary particle swarm optimization to identify marker genes for cancer classification. Through this implementation, the proposed feature selection model will involve all the features of a P system including communication rule, division rule, parallelism, and nondeterminism.

A Unified Feature Selection Model for High Dimensional Clinical Data Using Mutated Binary Particle Swarm Optimization and Genetic Algorithm

2021 ◽  
pp. 50-64
Author(s):  
Thendral Puyalnithi ◽  
Madhuviswanatham Vankadara
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Clinical Data ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Data Sets ◽  
Data Set ◽  
Swarm Optimization ◽  
Binary Genetic Algorithm

This article contends that feature selection is an important pre-processing step in case the data set is huge in size with many features. Once there are many features, then the probability of existence of noisy features is high which might bring down the efficiency of classifiers created out of that. Since the clinical data sets naturally having very large number of features, the necessity of reducing the features is imminent to get good classifier accuracy. Nowadays, there has been an increase in the use of evolutionary algorithms in optimization in feature selection methods due to the high success rate. A hybrid algorithm which uses a modified binary particle swarm optimization called mutated binary particle swarm optimization and binary genetic algorithm is proposed in this article which enhanced the exploration and exploitation capability and it has been a verified with proposed parameter called trade off factor through which the proposed method is compared with other methods and the result shows the improved efficiency of the proposed method over other methods.

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

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