1 research outputs found
Embedded Chaotic Whale Survival Algorithm for Filter-Wrapper Feature Selection
Classification accuracy provided by a machine learning model depends a lot on
the feature set used in the learning process. Feature Selection (FS) is an
important and challenging pre-processing technique which helps to identify only
the relevant features from a dataset thereby reducing the feature dimension as
well as improving the classification accuracy at the same time. The binary
version of Whale Optimization Algorithm (WOA) is a popular FS technique which
is inspired from the foraging behavior of humpback whales. In this paper, an
embedded version of WOA called Embedded Chaotic Whale Survival Algorithm
(ECWSA) has been proposed which uses its wrapper process to achieve high
classification accuracy and a filter approach to further refine the selected
subset with low computation cost. Chaos has been introduced in the ECWSA to
guide selection of the type of movement followed by the whales while searching
for prey. A fitness-dependent death mechanism has also been introduced in the
system of whales which is inspired from the real-life scenario in which whales
die if they are unable to catch their prey. The proposed method has been
evaluated on 18 well-known UCI datasets and compared with its predecessors as
well as some other popular FS methods.Comment: 28 pages, 6 figures, submitted a minor revision to Soft Computing,
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