Working with Classification Tools
Loading Data
 
The learning algorithm GeneXproTools 4.0 uses for classification, classifies your input data into two classes: class "0" and class "1". Obviously, the dependent variable 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
...
Cn versus Not Cn

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

But before evolving a model with GeneXproTools 4.0 you must first load the input data for the learning algorithm. GeneXproTools 4.0 allows you to work either with databases/Excel or text files and, for text files, accepts two different data matrix formats.

The first is the standard Samples x Variables format where samples are in rows and variables in columns, with the dependent variable occupying the rightmost position. In the small example below with 10 samples, IRIS_PLANT is the class and SEPAL_LENGTH, SEPAL_WIDTH, PETAL_LENGTH, and PETAL_WIDTH are the independent variables or attributes:

SEPAL_LENGTH SEPAL_WIDTH PETAL_LENGTH PETAL_WIDTH IRIS_PLANT
5.4 3.4 1.7 0.2 0
6.1 3.0 4.6 1.4 0
5.0 3.4 1.6 0.4 0
5.2 3.5 1.5 0.2 0
5.1 3.7 1.5 0.4 0
5.5 2.4 3.7 1.0 0
7.2 3.2 6.0 1.8 1
6.3 2.7 4.9 1.8 1
7.7 3.8 6.7 2.2 1
4.8 3.4 1.9 0.2 0

And the second, is the Gene Expression Matrix format commonly used in DNA microarrays studies where samples are in columns and variables in rows, with the class occupying the topmost position. For instance, in Gene Expression Matrix format, the small dataset above corresponds to:

IRIS_PLANT 0 0 0 0 0 0 1 1 1 0
SEPAL_LENGTH 5.4 6.1 5.0 5.2 5.1 5.5 7.2 6.3 7.7 4.8
SEPAL_WIDTH 3.4 3.0 3.4 3.5 3.7 2.4 3.2 2.7 3.8 3.4
PETAL_LENGTH 1.7 4.6 1.6 1.5 1.5 3.7 6.0 4.9 6.7 1.9
PETAL_WIDTH 0.2 1.4 0.4 0.2 0.4 1.0 1.8 1.8 2.2 0.2

which is very handy for datasets with a relatively small number of samples and thousands of variables. Note, however, that for Excel files this format is not supported and if your data is kept in this format in Excel, you must copy it to a text file so that it can be loaded into GeneXproTools.

GeneXproTools uses the Samples x Variables format throughout and therefore all formats are automatically converted and shown in this format.

GeneXproTools supports the standard separators (space, tab, comma, semicolon, and pipe) and detects them automatically. The use of labels to identify your variables is optional and GeneXproTools also detects automatically whether they are present or not. If you use them, however, you will be able to generate more intelligible code where each variable is identified by its name, by checking the Use Labels box in the Model Panel.

To Load Input Data for Modeling

  1. Click the File Menu and then choose New.
    The New Run Wizard appears. You must give a name to your new run file (the default filename extension of GeneXproTools 4.0 run files is .gep) and then choose Classification in the Problem Category box and the kind of source file in the Data Source Type box. GeneXproTools 4.0 allows you to work either with Excel/databases or text files.
  2. Then go to the Training Data window by clicking the Next button.
    Choose the path for the training set by browsing the Open dialog box and choose the appropriate data matrix format. Irrespective of the data format used, GeneXproTools shows the loaded data in the standard Samples x Variables format, with the class or dependent variable occupying the rightmost position.
  3. Then go to the Testing Data window by clicking the Next button.
    Repeat the same steps of the previous point if you wish to use a testing set to evaluate the predictive accuracy of your model.
  4. Click the Finish button to save your new run file.
    The Save As dialog box appears and after choosing the directory where you want your new run file to be saved, the GeneXproTools modeling environment appears. Then you just have to click the Evolve button to create a model as GeneXproTools automatically chooses, from a gallery of templates, default settings that will enable you to evolve a model immediately.

In data mining, be it performed by learning algorithms or conventional statistical methods, it really pays to take a good look at your data before embarking on a complex, usually time consuming modeling process. It's true that evolutionary algorithms are particularly well equipped to deal with noisy data, but the better the data you feed them the better the models they produce.

GeneXproTools helps you find missing and invalid (usually nominal) values in your datasets and prompts you to fix them before they are used for modeling. But the preparation of a well balanced dataset should be done before loading the data into GeneXproTools, and we recommend you to particularly take care of the following:

  • Avoid using duplicated samples for they can bias the modeling process considerably.
  • Choose a well balanced dataset.
  • Choose a reasonable number of samples for training.
    An excessively large dataset will slow the modeling process unnecessarily. If you have access to huge databases it’s good practice to use the surplus samples for testing instead. A good rule of thumb consists of using about 8-10 samples for each independent variable in your training data.
  • Check your datasets carefully for inaccurate values. Typographical or measurement errors generally cause outliers that can be detected by graphing one variable at a time, a task that can be easily accomplished by GeneXproTools in the Data Panel.

The graphical visualization tools of GeneXproTools 4.0 make it easy to identify outliers, which may well represent errors in the data files. After loading your data into GeneXproTools, in the Data Panel you can visualize the distribution of values for each variable and also plot each independent variable against the dependent variable.


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