scholarly journals Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

2018 ◽  
Vol 145 ◽  
pp. 25-45 ◽  
Author(s):  
Majdi Mafarja ◽  
Ibrahim Aljarah ◽  
Ali Asghar Heidari ◽  
Abdelaziz I. Hammouri ◽  
Hossam Faris ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Feature Selection ◽  
Selection Problems ◽  
Grasshopper Optimization

Relationship between Naïve Bayes error and max-dependency criterion in feature selection problems

ICCKE 2013 ◽  
2013 ◽  
Author(s):  
Nafiseh Sedaghat ◽  
Mahmood Fathy ◽  
Mohammad-Hossein Modarressi
Keyword(s):  
Feature Selection ◽  
Ve Bayes ◽  
Selection Problems ◽  
Bayes Error

scholarly journals IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 108032-108051
Author(s):  
Jiahui Li ◽  
Hui Kang ◽  
Geng Sun ◽  
Tie Feng ◽  
Wenqi Li ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Feature Selection ◽  
Dragonfly Algorithm ◽  
Adaptive Crossover

An Enhancing Grasshopper Optimization for Efficient Feature Selection

2020 ◽  
pp. 161-172
Author(s):  
Trong-The Nguyen ◽  
Shi-Jie Jiang ◽  
Thi-Kien Dao ◽  
Truong-Giang Ngo ◽  
Thi-Thanh-Tan Nguyen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Grasshopper Optimization

scholarly journals An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems

2018 ◽  
Vol 154 ◽  
pp. 43-67 ◽  
Author(s):  
Hossam Faris ◽  
Majdi M. Mafarja ◽  
Ali Asghar Heidari ◽  
Ibrahim Aljarah ◽  
Ala’ M. Al-Zoubi ◽  
...  
Keyword(s):  
Feature Selection ◽  
Salp Swarm Algorithm ◽  
Selection Problems ◽  
Swarm Algorithm

scholarly journals Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

2018 ◽  
Vol 10 (3) ◽  
pp. 478-495 ◽  
Author(s):  
Ibrahim Aljarah ◽  
Ala’ M. Al-Zoubi ◽  
Hossam Faris ◽  
Mohammad A. Hassonah ◽  
Seyedali Mirjalili ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

scholarly journals Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm

Genes ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 717
Author(s):  
Garba Abdulrauf Sharifai ◽  
Zurinahni Zainol
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Imbalanced Data ◽  
High Dimensional ◽  
Data Sets ◽  
Biomedical Data ◽  
Data Set ◽  
Grasshopper Optimization Algorithm ◽  
Imbalanced Class ◽  
Grasshopper Optimization

The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.

A hybrid mine blast algorithm for feature selection problems

2020 ◽  
Author(s):  
Mohammed Alweshah ◽  
Saleh Alkhalaileh ◽  
Dheeb Albashish ◽  
Majdi Mafarja ◽  
Qusay Bsoul ◽  
...  
Keyword(s):  
Feature Selection ◽  
Selection Problems ◽  
Mine Blast Algorithm

An improved runner-root algorithm for solving feature selection problems based on rough sets and neighborhood rough sets

2020 ◽  
Vol 97 ◽  
pp. 105517 ◽  
Author(s):  
Rehab Ali Ibrahim ◽  
Mohamed Abd Elaziz ◽  
Diego Oliva ◽  
Songfeng Lu
Keyword(s):  
Feature Selection ◽  
Rough Sets ◽  
Selection Problems ◽  
Neighborhood Rough Sets
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