Modified cuckoo search algorithm with rough sets for feature selection

2016 ◽  
Vol 29 (4) ◽  
pp. 925-934 ◽  
Author(s):  
Mohamed Abd El Aziz ◽  
Aboul Ella Hassanien
Keyword(s):  
Feature Selection ◽  
Rough Sets ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm

scholarly journals Memory Based Cuckoo Search Algorithm for Feature Selection of Gene Expression Dataset

2021 ◽  
pp. 100572
Author(s):  
Malek Alzaqebah ◽  
Khaoula Briki ◽  
Nashat Alrefai ◽  
Sami Brini ◽  
Sana Jawarneh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Gene Expression Dataset ◽  
Selection Of

scholarly journals Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Maoxian Zhao ◽  
Yue Qin
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Binary Classification ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Feature Weighting ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Elite Strategy ◽  
Low Dimensional

For the low optimization accuracy of the cuckoo search algorithm, a new search algorithm, the Elite Hybrid Binary Cuckoo Search (EHBCS) algorithm, is improved by feature weighting and elite strategy. The EHBCS algorithm has been designed for feature selection on a series of binary classification datasets, including low-dimensional and high-dimensional samples by SVM classifier. The experimental results show that the EHBCS algorithm achieves better classification performances compared with binary genetic algorithm and binary particle swarm optimization algorithm. Besides, we explain its superiority in terms of standard deviation, sensitivity, specificity, precision, and F -measure.

Improved Cuckoo Search Algorithm for Future Selection

2021 ◽  
Vol 9 (2) ◽  
pp. 113-123
Author(s):  
T. Mathi Murugan ◽  
◽  
E. Baburaj ◽  
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
High Dimensional ◽  
Svm Classifier ◽  
Knn Classifier ◽  
Experimental Process ◽  
Improved Cuckoo Search Algorithm

The classification of high-dimensional dataset is challenging as it contains large amount irrelevant and noisy features. Thus, feature selection is performed in the dataset to eliminate these redundant features. It reduces the dimensionality of the dataset and increases the classification accuracy. Hence, for selecting the relevant features in high dimensional data, an improved cuckoo search algorithm (ICSA) was proposed in this paper. After feature selection, the dataset undergo classification using KNN classifier and SVM classifier. The experimental process illustrates that the improved cuckoo search algorithm effectively increases the classification accuracy by reducing the number of features in the dataset. For analysing the proposed algorithm, seven UCI repository dataset have been utilised. Also, the ICS algorithm is compared with other existing algorithms for the given dataset. From the investigation process, it was concluded that the proposed algorithm selects lesser number of features and also enhances the classification accuracy than the other existing algorithms.

BCS: A Binary Cuckoo Search algorithm for feature selection

2013 ◽  
Author(s):  
D. Rodrigues ◽  
L. A. M. Pereira ◽  
T. N. S. Almeida ◽  
J. P. Papa ◽  
A. N. Souza ◽  
...  
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm

scholarly journals Cuckoo inspired algorithms for feature selection in heart disease prediction

2018 ◽  
Vol 4 (2) ◽  
pp. 95 ◽  
Author(s):  
Ali Muhammad Usman ◽  
Umi Kalsom Yusof ◽  
Syibrah Naim
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Prediction Accuracy ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Diagnostic Techniques ◽  
Cuckoo Search Algorithm ◽  
Filter Method ◽  
Learning Approaches ◽  
Common People

Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets.

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