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Genna
Overview | Genna
Uniqueness | Genna
Key features | Genna
Userbase | Genna
System Requirements
Genna
Key Features
Attribute Weights: User
or (Semi) Automatic Generation
GENNA provides the user with three options for optimising
the similarity metric used in comparable retrieval.
In the first instance the user of the algorithm
can provide a weighting for each of the dependent
variables in the input data. Secondly, the user
can provide a ranking of the attributes based on
domain knowledge resident in the user.
This ranking is taken into account by the optimisation
carried out by the Genetic Algorithm within GENNA.
Finally, the user can suggest that the Genetic Algorithm
generate the weights autonomously. After the generation
of the weights the user can tune the weights and
generate new models to obtain insights into the
sensitivity of the model to changes in the individual
attribute weights.
Flexibility
GENNA provides the user with greater flexibility
with regards to affecting the type of model that
is generated through the setting of parameters of
the algorithm that affect the nature of the distance
metric employed, the prediction method employed
and the number of comparables used within the prediction
phase of the model. The user can also influence
the type of error distribution generated by the
application of the model through the setting of
a parameter that affects the trade-off used by the
algorithm between accuracy and variability of the
model. The algorithm uses well-established statistical
metrics to generate a measure the estimated accuracy
and variability of the model.
In addition to the above mentioned features GENNA
also provides the following unique features:
Ability
to use Censored Observations
Ability
to use Categorical and Numeric Attributes through
the use of innovative distance metrics
Using Categorical and Numeric Attributes
Automatic Indexing of data for Scalability and Speed
Incremental Learning and Introspection.
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Coral
Summary
