Fitness Functions and the Rounding Threshold

Working with Classification Tools

The learning algorithm that GeneXproTools 4.0 uses for classification, classifies your input data into two classes: class "0" and class "1". Obviously, the dependent variable or class in your training and testing sets can only have two distinct values: 0 or 1.

GeneXproTools 4.0 classifies the value returned by the evolved model as "1" or "0" using the 0/1 Rounding Threshold. If the value returned by the evolved model is equal to or greater than the rounding threshold, then the record is classified as "1", "0" otherwise.

Classification problems with more than two classes are also easily solved with GeneXproTools 4.0. When you are classifying data into more than two classes, say, n distinct classes, you must decompose your problem into n separate 0/1 classification tasks as follows:

C₁ versus Not C₁
C₂ versus Not C₂
...
C_n versus Not C_n

Then evolve n different models separately and combine the different models to make the final classification model.

For Classification problems, in the Fitness Function Tab of the Settings Panel you have access to 30 built-in fitness functions. Additionally, you can also design your own custom fitness function and explore the solution space with it. In all cases, though, you must always take into consideration the 0/1 rounding threshold.

You can design your own custom fitness function using the Custom Fitness Function window to write the code of your fitness function. The code for the custom fitness function must be in JavaScript and can be tested before evolving a model with it by pressing the Test button.

The 30 built-in fitness functions of GeneXproTools 4.0 for Classification are based on the:

Number of Hits
Hits with Penalty
Accuracy
Squared Accuracy
Sensitivity/Specificity
PPV/NPV
SSPN
MSE (mean squared error)
RMSE (root mean squared error)
MAE (mean absolute error)
RSE (relative squared error)
RRSE (root relative squared error)
RAE (relative absolute error)
R-square
Correlation Coefficient
And all the above with parsimony pressure

The kind of fitness function you choose will depend most probably on the indicator you are most familiar with. And although there is nothing wrong with this, for all of them can accomplish an efficient evolution, you might want to try different fitness functions for they travel the fitness landscape differently: some of them very straightforwardly in their pursuits while others choose less obvious paths.

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