Efficient Feature Selection Method for Histopathological Images Using Modified Golden Eagle Optimization Algorithm

2021 ◽  
Author(s):  
Surbhi Vijh ◽  
Sumit Kumar ◽  
Mukesh Saraswat
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Golden Eagle ◽  
Histopathological Images

scholarly journals Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

2019 ◽  
Vol 1 (5) ◽  
Author(s):  
Maryam Shuaib ◽  
Shafi’i Muhammad Abdulhamid ◽  
Olawale Surajudeen Adebayo ◽  
Oluwafemi Osho ◽  
Ismaila Idris ◽  
...  
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Whale Optimization Algorithm ◽  
Rotation Forest ◽  
Whale Optimization ◽  
Email Spam

Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm

2020 ◽  
pp. 407-421
Author(s):  
Noria Bidi ◽  
Zakaria Elberrichi
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Adaptive Algorithm ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Biomedical Data ◽  
Features Selection ◽  
Benchmark Datasets

This article presents a new adaptive algorithm called FS-SLOA (Feature Selection-Seven Spot Ladybird Optimization Algorithm) which is a meta-heuristic feature selection method based on the foraging behavior of a seven spot ladybird. The new efficient technique has been applied to find the best subset features, which achieves the highest accuracy in classification using three classifiers: the Naive Bayes (NB), the Nearest Neighbors (KNN) and the Support Vector Machine (SVM). The authors' proposed approach has been experimented on four well-known benchmark datasets (Wisconsin Breast cancer, Pima Diabetes, Mammographic Mass, and Dermatology datasets) taken from the UCI machine learning repository. Experimental results prove that the classification accuracy of FS-SLOA is the best performing for different datasets.

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