Fire Fly Based Feature Selection Approach
Irrelevant, noisy and high dimensional data, containing large
number of features, degrades the performance of data mining and
machine learning tasks. One of the methods used in data mining
to reduce the dimensionality of data is feature selection. Feature
selection methods select a subset of features that represents
original features in problem domain with high accuracy. Various
methods have been proposed that utilize heuristic or nature
inspired strategies along with Rough Set Theory (RST) to find
these subsets. However these methods either consume more time
to find subset or compromise with optimality. The paper presents
a new feature selection approach that combines the RST with
nature inspired ‘firefly’ algorithm. The algorithm simulates the
attraction system of real fireflies that guides the feature selection
procedure. The experimental result proves that the proposed
algorithm scores over other feature selection method in terms of
time and optimality.
Keywords: Feature Selection, Rough Set, Firefly Algorithm
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