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Asked: 2026-06-14T22:09:15+00:00

I have painstakingly gathered data for a proof-of-concept study I am performing. The data

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I have painstakingly gathered data for a proof-of-concept study I am performing. The data consists of 40 different subjects, each with 12 parameters measured at 60 time intervals and 1 output parameter being 0 or 1. So I am building a binary classifier.

I knew beforehand that there is a non-linear relation between the input-parameters and the output so a simple perceptron of Bayes classifier would be unable to classify the sample. This assumption proved correct after initial tests.

Therefore I went to neural networks and as I hoped the results were pretty good. An error of about 1-5% is generally the result. The training is done by using 70% as training and 30% as evaluation. Running the complete dataset again (100%) through the model I was very happy with the results. The following is a typical confusion matrix (P = positive, N = negative):

    P    N
P  13    2   
N   3   42

So I am happy and with the notion that I used a 30% for evaluation I am confident that I am not fitting noise.

Therefore I resolved to SVM for a double check and the SVM was unable to converge to a good solution. Most of the time the solutions are terrible (say 90% error…). Maybe I am not fully aware of SVM’s or the implementations are not correct, but it troubles me because I thought that when NN provide a good solution, SVM’s are most of the time better in seperating the data due to their maximum-margin hyperplane.

What does this say of my result? Am I fitting noise? And how do I know if this is a correct result?

I am using Encog for the calculations but the NN results are comparable to home-grown NN models I made.

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1 Answer

  1. Editorial Team

    If it is your first time to use SVM, I strongly recommend you to take a look at A Practical Guide to Support Vector Classication, by authors of a famous SVM package libsvm. It gives a list of suggestions to train your SVM classifier.

    • Transform data to the format of an SVM package
    • Conduct simple scaling on the data
    • Consider the RBF kernel
    • Use cross-validation to nd the best parameter C and γ
    • Use the best parameter C and γ
    • to train the whole training set
    • Test

    In short, try scaling your data and carefully choosing the kernal plus the parameters.

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