Neural Networks reference

Biopsy Machine Learning
Leo Liberti: Professeur Chargé

I was looking through the datasets of the R-CRAN package MASS to find one that I thought looked interesting to use in a machine learning context and I found ‘biopsy’. Potential benefits of using these data for testing machine learning algorithms include:

  1. The ouput (class of tumour) is binary
  2. The inputs are all scaled from 1 to 10
  3. There are quite a few samples (N=699)

Another dataset that looks interesting is ‘Boston’, which I might look at another time.

My aim is to train a feed-forward neural network using backpropagation with the input attributes of clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion, single epithelial cell size, bare nuclei, bland chromatin, normal nucleoli and mitosis to predict the output class being either ‘benign’ or ‘malignant’. I probably should have looked at the references that are mentioned along with the documentation to get an idea of whether or not some attributes could be discarded because they don’t significantly affect the class of tumour. However, I decided to just jump straight in partly because I would like to try something with a lot of inputs (to see, for example, how well my code scales).

I don’t have any of my own machine learning functions written for R, so I saved the dataset to a text file that I could use with the Octave functions that I had written a few weeks ago. From the R command prompt I load the MASS library, then convert the biopsy class so that benign tumours get the value 0 and malignant tumours get the value 1. This conversion is done so that Octave reads the data in properly, since matrices must have elements of all the same data type. Then I wrote the data to file, excluding the first column which is a non-unique sample code number.

Source: SAW Cordwell

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