Neural Net Classifier Architecture Testing

Having successfully integrated the FANN library I've changed my mind as to the design of my neural net classifier. Previously I had planned to train a series of classifiers, each "tuned" to a specific measured period in the data, and each to discriminate between 5 distinct market types - my normal cyclic, uwr, unr, dwr and dnr as I've talked about in earlier posts.

Now, however, I'm working on a revised design that incorporates elements of a decision tree, where neural nets sit at interior nodes of the tree. A simple schematic is shown below

The advantage of this model is that I only have to train 5 neural nets, represented by the 5 colours in the above schematic, and each net is a binary classifier (-1 and 1) with only one node in its output layer, with a decision rule of > 0 or < 0 for the activation function. Rather than have one neural net per period, the period is now one of the features in the features input vector.

At the moment the features vector has a length of 48, and as I write this my computer is churning through a cross validation test (using the FANN library) to determine the optimum number of nodes for the single hidden layer(s) of the neural nets. Once this is complete, I plan to run another series of cross validation tests to determine the optimum number of epochs to run during training. When these are complete I shall then run one final cross validation test to determine the optimum lambda for a regularisation term. This last set of tests will use the Octave code previously used because, as far as I can see, FANN library functions do not appear to have this capability.

One slight change to this Octave code will be to use the Hyberbolic tangent as the activation function (see Y. LeCun 1998, available as paper 86 here). I may also try to implement some other tricks recommended in this paper.