Abstract:
Taxonomy is the science of naming, describing
and classifying organisms that includes all plants,
animals and microorganisms of the world. Using
morphological, behavioral, genetic information and
biochemical observations, taxonomists identify and
describe species into classification. The taxonomic
identification of fishes is a time-consuming process
and making errors is indispensable for those who are
not specialists. This system proposes an automated
species identification system to identify taxonomic
characters of species based on specimen and provide
statistical clues for assisting taxonomists to identify
accurate species or revision of misdiagnosed species.
For this system, feature selection is an essential
step to effectively reduce data dimensionality.
This system first selects the best relevant features
by using combination and the classification
performance of two classifiers, Random Forest and
Attributed Selected. And then correctly classifies the
fish species and compares the accuracy of these two
classifiers.
