_PROBLEM CoEPrA-2006_Classification_003

_GROUP_NAME Francisco Melo

_GROUP_MEMBERS
Francisco Melo
J.T. Eterovic
Evandro Ferrada
Tomas Norambuena
Rodrigo Malig
Ismael Vergara

_ADDRESS
fmelo@bio.puc.cl

_MODELING_PROCEDURE

We first applied feature selection using the algorithm described by Koller
 and Sahami in "Toward Optimal Feature Selection" (1996) at
 each position/aa of the nonapeptide (i.e., for each matrix of
 133 raws x 643 columns). We decided to use it because of its capacity
 to handle large amounts of data and because it considers redundance among
 features, and the relevance of association for each feature with the class.

After this initial procedure, we selected 287 features from the initial
 set of the 5787 features.

To derive the optimal solution we have used a genetic algorithm (GA)
 that evolves mathematical functions that are used
as linear and/or non-linear transformations of the feature space.
 The GA has been developed in our lab and it was
executed with the following parameters or settings:

- mutation rate: 0.1 
- cross over: 1.0
- number of individuals: 500
- number of iterations: 1000
- elitism size: 2.0
- max depth of the trees generated: 3
- lineal normalization with parameter 1.0

The GA generated the following optimal solution:
 Transform = Desc_935 - Desc4998 - Desc_4431*Desc_309

The accuracy obtained for the calibration data set was 0.78.

The optimal threshold to classify between labels 1 and -1 was -1.782.
 This threshold was used to classify the
instances in the prediction data set.


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