scholarly journals RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach

2017 ◽  
Vol 161 (6) ◽  
pp. 1-5 ◽  
Author(s):  
Anupriya Gupta ◽  
Anuradha Purohit
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Rough Set ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Abi Rafdi ◽  
Herman Mawengkang Herman ◽  
Syahril Efendi
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Bayes Algorithm

This study analyzes Sentiment to see opinions, points of view, judgments, attitudes, and emotions towards creatures and aspects expressed through texts. One of Social Media is like Twitter is one of the most widely used means of communication as a research topic. The main problem with sentiment analysis is voting and using the best feature options for maximum results. Either, the most widely known classification method is Naive Bayes. However, Naive Bayes is very sensitive to significant features. That way, in this test, a comparison of feature selection is carried out using Particle Swarm Optimization and Genetic Algorithm to improve the accuracy performance of the Naive Bayes algorithm. Analyses are performed by comparing before and after testing using feature selection. Validation uses a cross-validation technique, while the confusion matrix ??is appealed to measure accuracy. The results showed the highest increase for Naïve Bayes algorithm accuracy when using the feature selection of the Particle Swarm Optimization Algorithm from 60.26% to 77.50%, while the genetic algorithm from 60.26% to 70.71%. Therefore, the choice of the best characteristics is Particle Swarm Optimization which is superior with an increase in accuracy of 17.24%.

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.

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