Improved Binary Sailfish Optimizer Based on Adaptive <italic>&#x03B2;</italic>-Hill Climbing for Feature Selection

Improved Binary Sailfish Optimizer Based on Adaptive <italic>&#x03B2;</italic>-Hill Climbing for Feature Selection

Feature selection (FS), an important pre-processing step in the fields of machine learning and data mining, has immense impact on the outcome of the corresponding learning models. Basically, it aims to remove all possible irrelevant as well as redundant features from a feature vector, thereby enhanc...

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Bibliographic Details
Main Authors: Kushal Kanti Ghosh, Shameem Ahmed, Pawan Kumar Singh, Zong Woo Geem, Ram Sarkar
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Binary sailfish optimizer
feature selection
adaptive <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">β</italic>-hill climbing
hybrid optimization
UCI dataset
Online Access:https://ieeexplore.ieee.org/document/9082591/

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https://ieeexplore.ieee.org/document/9082591/

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