  One of the major problems in computational biology is the inability of
existing classification models to incorporate expanding and new domain
knowledge. This problem of static classification models is addressed in this
paper by the introduction of incremental learning for problems in
bioinformatics. Many machine learning tools have been applied to this problem
using static machine learning structures such as neural networks or support
vector machines that are unable to accommodate new information into their
existing models. We utilize the fuzzy ARTMAP as an alternate machine learning
system that has the ability of incrementally learning new data as it becomes
available. The fuzzy ARTMAP is found to be comparable to many of the widespread
machine learning systems. The use of an evolutionary strategy in the selection
and combination of individual classifiers into an ensemble system, coupled with
the incremental learning ability of the fuzzy ARTMAP is proven to be suitable
as a pattern classifier. The algorithm presented is tested using data from the
G-Coupled Protein Receptors Database and shows good accuracy of 83%. The system
presented is also generally applicable, and can be used in problems in genomics
and proteomics.
