A Modified Marine Predator Algorithm for an Efficient Feature Selection

Abdulrazaq Ahmed Bello, Muhammad Abdullahi, Salisu Aliyu

Abstract


Feature Selection (FS) is considered an important but yet a difficult
data pre-processing task. Metaheuristics algorithm (MA) such as
Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA),
Dragonfly Algorithm (DA), Grey Wolf Optimization (GWO) and Marine
Predator Algorithm (MPA) are used for optimizing feature selection
task. The Marine Predator Algorithm is a population based algorithm
modeled after the foraging actions of marine predators. MPA is an
efficient population based MA but has limitations in FS related task.
These limitations include its method of initial population generation
which is random initialization and suffers from diversity. Another
limitation is exploration/exploitation phase imbalance which result to
the search been stuck in local optima, that is, imbalance between the
global search and local search. Despite, several attempts by
researchers to address this challenges, these problems still persist.
This work proposes a novel population initialization mechanism in MPA
using logistic chaotic map. Ten benchmark datasets from the
University of California in Irvine (UCI) repository were used throughout
the experiment. The k-Nearest Neighbor (KNN) was used as the base
classifier. To address overfitting problem, each of the datasets was
divided into training and testing using K-fold cross-validation. Results
obtained from the experiments showed a significant improvement
against the traditional MPA.


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