An enhanced Cauchy mutation grasshopper optimization with trigonometric substitution: engineering design and feature selection

2021 ◽  
Author(s):  
Songwei Zhao ◽  
Pengjun Wang ◽  
Ali Asghar Heidari ◽  
Xuehua Zhao ◽  
Chao Ma ◽  
...  
Keyword(s):  
Feature Selection ◽  
Engineering Design ◽  
Cauchy Mutation ◽  
Grasshopper Optimization

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

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 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.

scholarly journals A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xianghua Chu ◽  
Shuxiang Li ◽  
Da Gao ◽  
Wei Zhao ◽  
Jianshuang Cui ◽  
...  
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Learning Strategy ◽  
Population Distribution ◽  
Learning Algorithm ◽  
Test Problems ◽  
Bee Colony ◽  
Cauchy Mutation ◽  
Optimal Classification ◽  
Improved Learning

This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.

Binary Grasshopper Optimization Based Feature Selection For Intrusion Detection System Using Feed Forward Neural Network Classifier

2020 ◽  
Vol 13 ◽  
Author(s):  
M. Jeyakarthic ◽  
A. Thirumalairaj
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Superior Performance ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Background: Due to the advanced improvement in internet and network technologies, significant number of intrusions and attacks takes place. An intrusion detection system (IDS) is employed to prevent distinct attacks. Several machine learning approaches has been presented for the classification of IDS. But, IDS suffer from the curse of dimensionality that results to increased complexity and decreased resource exploitation. Consequently, it becomes necessary that significant features of data must be investigated by the use of IDS for reducing the dimensionality. Aim: In this article, a new feature selection (FS) based classification system is presented which carries out the FS and classification processes. Methods: Here, the binary variants of the Grasshopper Optimization Algorithm called BGOA is applied as a FS model. The significant features are integrated using an effective model to extract the useful ones and discard the useless features. The chosen features are given to the feed forward neural network (FFNN) model to train and test the KDD99 dataset. Results: The validation of the presented model takes place using a benchmark KDD Cup 1999 dataset. By the inclusion of FS process, the classifier results gets increased by attaining FPR of 0.43, FNR of 0.45, sensitivity of 99.55, specificity of 99.57, accuracy of 99.56, Fscore of 99.59 and kappa value of 99.11. Conclusion: The experimental outcome ensured the superior performance of the presented model compared to diverse models under several aspects and is found to be an appropriate tool for detecting intrusions.

A novel quantum grasshopper optimization algorithm for feature selection

2020 ◽  
Vol 127 ◽  
pp. 33-53 ◽  
Author(s):  
Dong Wang ◽  
Hongmei Chen ◽  
Tianrui Li ◽  
Jihong Wan ◽  
Yanyong Huang
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization
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