Hybrid flower pollination algorithm with rough sets for feature selection

Citation:
Zawbaa, H. M., A. E. H. , and W. Y., E. Emary, "Hybrid flower pollination algorithm with rough sets for feature selection", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.

Date Presented:

30 Dec

Flower pollination algorithm (FPA) optimization is
a new evolutionary computation technique that inspired from the
pollination process of flowers. In this paper, a model for multiobjective
feature selection based on flower pollination algorithm
(FPA) optimization hybrid with rough set is proposed. The
proposed model exploits the capabilities of filter-based feature
selection and wrapper-based feature selection. Filter-based approach
can be described as data oriented methods that not
directly related to classification performance. Wrapper-based
approach is more related to classification performance but it does
not face redundancy and dependency among the selected feature
set. Therefore, we proposed a multi-objective fitness function that
uses FPA to the find optimal feature subset. The multi-objective
fitness function enhances classification performance and guarantees
minimum redundancy among selected features. At begin of
the optimization process, fitness function uses mutual information
among feature as a goal for optimization. While at some later time
and using the same population, the fitness function is switched
to be more classifier dependent and hence exploits rough-set
classifier as a guide to classification performance. The proposed
model was tested on eight datasets form UCI data repository
and proves advance over other search methods as particle swarm
optimization (PSO) and genetic algorithm (GA).

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