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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
| ABSTRACT - Internet Electron. J. Mol. Des. September 2004, Volume 3, Number 9, 572-585 |
An Ant Colony Optimization-based Classifier System for Bacterial Growth
Prakash S. Shelokar, Valadi K. Jayaraman, and Bhaskar D. Kulkarni
Internet Electron. J. Mol. Des. 2004, 3, 572-585
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Abstract:
In predictive microbiology, identification of different combination
of environmental factors (such as temperature, water activity, pH),
which lead to growth/ no-growth of microorganism, is a problem
of potential importance. Ant colony optimization (ACO) is one of
the most recently developed nature-inspired metaheuristic
techniques, based on the foraging behavior of real life ants and has
already exhibited superior performance in solving combinatorial
optimization problems. This work explores the search capabilities
of this metaheuristic for learning classification rules in bacterial
growth/no growth data pertaining to pathogenic Escherichia coli
R31 as affected by temperature and water activity. The discovered
rules thus can be used to verify whether any combination of
temperature and water activity belong to either growth or no-growth
of the microorganism. The ant algorithm for classification
works iteratively as follows: At any iteration level, software ants
construct rules using available heuristic information and
dynamically evolved pheromone trails. A rule that has highest
prediction quality is said to be a discovered rule, which represents
information extracted from the database. Examples correctly
covered by the discovered rule are removed from the training set,
and another iteration is started. Guided by the modified pheromone
matrix, the agents build improved rules and the process is repeated
for as many iterations as necessary to find rules covering almost all
cases in the training set. The developed ACO classifier system is
utilized on several datasets and its performance is compared with
the performance of other well known algorithms in terms of the
average accuracy attained in 10-fold cross validation. The results
obtained by this algorithm compare very favorably with other
classifiers. Additionally, for discovery of classification rules in the
dataset pertaining to bacterial growth/no-growth, the performance
of the ACO classifier is compared with the C4.5 system with
respect to the predictive accuracy and the simplicity of discovered
rules. In both these performance indices the ACO classifier
compares very well with the C4.5. The results obtained on several
data sets indicate that the algorithm is competitive and can be
considered a very useful tool for knowledge discovery in a given
database.
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