Let’s say I have tens of thousands of datasets (rows) consisting each of 300 columns of integer, double and strings (no image, no audio). Five of these target columns represent interesting resulting properties (integer) of the product that is represented by of each dataset (row) that I want to predict.
My current idea is to create classes for each target column and combine these classes: column1 has classes 1-5, column2 has also class 1-5… so I get combined target classes in the form “11111”, “15312”, “43151”, “55555”. Now I have only one target-“label” for each dataset (row) that could be the output of an ANN. This approach has at least two disadvantages:
- some combined classes have only a small number of datasets and
- the binning of each column in only five classes decreases the precision.
If I increase the number classes of each column, I get even a smaller number of datasets in some combined classes.
So my question is if I should go on with the approach or are there better ideas? It is possible to use ANN to predict several properties (i.e. classes) at the same time?