The deliverables for the assignment are below. I will
attach
the assignment as well as the data needed.
Deliverable : ALL the requested results (numbers, ROC curves, observations); as well as your conclusions on each section as comments in the code. Also, compress and submit ALL your saved neural network objects and programs (but not the training data).
MiniProj3 RBF Classification You will use radial basis function neural networks as classifiers for breast cancer detection. The dataset is as follows: First, read the dataset description at: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29 For your convenience, I have reformatted the data for the MATLAB. The input matrix is called P, each row of which corresponds to different measurements of a patient’s tumor cell sample. The corresponding element in the output vector T is -1 if the cell was determined to be benign, and +1 if malignant. Load P and T into MATLAB’s by using the menu “File > Import Data” option, or by simply dragging and dropping. Creating RBF neural nets: load P and T into MATLAB, and divide it into training, validation, and test subsets (60%, 20%, and 20% ratios) using the following command [trainP,valP,testP,trainInd,valInd,testInd] = dividerand(P,0.6,0.2,0.2); [trainT,valT,testT] = divideind(T,trainInd,valInd,testInd); Now create two RBF neural nets, exact and regular, using net1 = newrbe(trainP,trainT,SPREAD1); net2 = newrb(trainP,trainT,GOAL,SPREAD2); Please read MATLAB’s help documentation for newrbe and newrb for more information. You can find the output and MSE error for each network by (e.g. validation, network 1) y1v = sim(net1,valP); mse1v=mse(y1v-valT); Or for network 2 training: y2tr= sim(net2,trainP); mse2tr=mse(y2tr-trainT) Note that you have to choose a SPREAD (and GOAL, for newrb) first. Do this: change the values of SPREAD (and GOAL, for newrb) until you get a validation MSE of 0.75. Report on your observations. Specify the network (rb or rbe, SPREAD, size, and GOAL) that got you to that point. Report the test MSE of your final solution. Plot the training, validation, and test ROC curves for the above network. Note: you may want to start with larger spreads, e.g. 10 or more, and use a loop for next values. Deliverable : ALL the requested results (numbers, ROC curves, observations); as well as your conclusions on each section. Also, compress and submit ALL your saved neural network objects and programs (but not the training data). P.mat P:[30x569 double array] T.mat T:[1x569 double array]