An improved image steganalysis using a novel feature selection algorithm based on artificial bee colony

2013 ◽  
pp. 609-614
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Image Steganalysis ◽  
Bee Colony

Artificial Bee Colony–Based Feature Selection Algorithm for Cyberbullying

2020 ◽  
Author(s):  
Esra Sarac Essiz ◽  
Murat Oturakci
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Selection Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Traditional Methods ◽  
Bee Colony ◽  
Nature Inspired Algorithm

Abstract As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.

An Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm for Twitter Sentiment Analysis

2020 ◽  
Vol 17 (12) ◽  
pp. 5378-5385
Author(s):  
S. Kasthuri ◽  
A. Nisha Jebaseeli
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Error Rates ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Bee Colony ◽  
Improved Performance

Twitter Sentiment Study is a difficult task that comprises the various kind of preprocessing phases, including reduction in dimensionality. The reduction in dimensionality ensures minimum computational complexity and improved performance in the classification course. In Twitter data, each tweet has functionality values that may or may not reflect an individual’s response. As a result, when tweets are signified as feature matrices, many sparse data points are created and possibly overhead and error rates increase in sentiment analysis on Twitter. This paper proposes a novel kind of algorithm as Artificial Bee Colony and Pigeon Inspired Optimization Hybrid Feature Selection Algorithm. The ABC-PIO combines with the characteristics that ABC can produce various samples, PIO can reach the best value rapidly and Cauchy perturbation strategy can improve optimal solution. The proposed technique archive Accuracy of 99.01% for Decision tree, 77.34% for Navy Bias and 60.89% Random Forest. The comparative analysis show that the proposed ABC-PIO with Decision tree archive much better results compared to other existing techniques.

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