keywords = "genetic algorithms, genetic programming, biological
systems, data classification, data driven evolutionary
modeling approach, genetic programming models,
illustrative datasets, linear classifiers, nonlinear
classifiers, nonlinear interactions, biology computing,
data handling",
DOI = "doi:10.1109/CEC.2007.4425013",
ISBN = "1-4244-1340-0",
file = "1827.pdf",
abstract = "Data classification problems especially for biological
systems pose serious challenges mainly due to the
presence of multivariate and highly nonlinear
interactions between variables. Specimens that need to
be distinguished from one another show similar profiles
and cannot be separated easily based on decision
boundaries or available discriminating rules.
Alternatively, inter-relations among the feature
vectors can be exploited for distinguishing samples
into specific classes. Such variable interaction models
are difficult to establish given the nature of
biological systems. Genetic Programming, a data driven
evolutionary modelling approach is proposed here to be
a potential tool for designing variable dependency
models and exploiting them further for class
discrimination. A new and alternative GP model based
classification approach is proposed. Analysis is
carried out using illustrative datasets and the
performance is bench marked against well established
linear and nonlinear classifiers like LDA, kNN, CART,
ANN and SVM. It is demonstrated that GP based models
can be effective tools for separating unknown
biological systems into different classes. The new
classification method has the potential to be
effectively and successfully extended to many systems
biology applications of recent interest.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.