Feature Selection by Firefly Algorithm with Improved Initialization Strategy

2021 ◽  
Author(s):  
Timea Bezdan ◽  
Dusan Cvetnic ◽  
Luka Gajic ◽  
Miodrag Zivkovic ◽  
Ivana Strumberger ◽  
...  
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Initialization Strategy

scholarly journals Feature Selection Models Based on Hybrid Firefly Algorithm with Mutation Operator for Network Intrusion Detection

2021 ◽  
Vol 14 (1) ◽  
pp. 192-202
Author(s):  
Karrar Alwan ◽  
◽  
Ahmed AbuEl-Atta ◽  
Hala Zayed ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Firefly Algorithm ◽  
Detection System ◽  
Binary Classification ◽  
Feature Reduction ◽  
Detection Accuracy ◽  
Multi Classification ◽  
Modified Firefly Algorithm

Accurate intrusion detection is necessary to preserve network security. However, developing efficient intrusion detection system is a complex problem due to the nonlinear nature of the intrusion attempts, the unpredictable behaviour of network traffic, and the large number features in the problem space. Hence, selecting the most effective and discriminating feature is highly important. Additionally, eliminating irrelevant features can improve the detection accuracy as well as reduce the learning time of machine learning algorithms. However, feature reduction is an NPhard problem. Therefore, several metaheuristics have been employed to determine the most effective feature subset within reasonable time. In this paper, two intrusion detection models are built based on a modified version of the firefly algorithm to achieve the feature selection task. The first and, the second models have been used for binary and multiclass classification, respectively. The modified firefly algorithm employed a mutation operation to avoid trapping into local optima through enhancing the exploration capabilities of the original firefly. The significance of the selected features is evaluated using a Naïve Bayes classifier over a benchmark standard dataset, which contains different types of attacks. The obtained results revealed the superiority of the modified firefly algorithm against the original firefly algorithm in terms of the classification accuracy and the number of selected features under different scenarios. Additionally, the results assured the superiority of the proposed intrusion detection system against other recently proposed systems in both binary classification and multi-classification scenarios. The proposed system has 96.51% and 96.942% detection accuracy in binary classification and multi-classification, respectively. Moreover, the proposed system reduced the number of attributes from 41 to 9 for binary classification and to 10 for multi-classification.

Improving firefly algorithm-based logistic regression for feature selection

2019 ◽  
Vol 22 (8) ◽  
pp. 1577-1581
Author(s):  
Mohammed Abdulrazaq Kahya ◽  
Suhaib Abduljabbar Altamir ◽  
Zakariya Yahya Algamal
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Firefly Algorithm

Feature selection using firefly algorithm in software defect prediction

2017 ◽  
Vol 22 (S5) ◽  
pp. 10925-10934 ◽  
Author(s):  
M. Anbu ◽  
G. S. Anandha Mala
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect

Modified Firefly Algorithm With Chaos Theory for Feature Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Sujata Dash ◽  
Ruppa Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
High Dimensional ◽  
Support Vector ◽  
Combinatorial Optimization Problems ◽  
Meta Search ◽  
Combined Algorithm

Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts.

scholarly journals Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2576 ◽  
Author(s):  
Aiming Liu ◽  
Kun Chen ◽  
Quan Liu ◽  
Qingsong Ai ◽  
Yi Xie ◽  
...  
Keyword(s):  
Feature Selection ◽  
Motor Imagery ◽  
Firefly Algorithm ◽  
Learning Automata ◽  
Eeg Classification ◽  
Selection For
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