# How to combine indicators resulting from a machine learning classification?

I've done a classification with different machine learning method, as result i've the confusion matrix and the roc curve. To find best classification model, i'm looking for a method to combine the indicators computed with the confusion matrix, for example i've thought :

score=efficiency+purity+completeness-contamination


or

score=(efficiency+purity+completeness)/contamination


and then find the model with the max score. What is the better criterion?

Let's say you train a neural network using cross-entropy to classify inputs into one of four categories. Let's say live input x_i yields:

[0 0.45 0.55 0]

Let's also say you train a Naive Bayes classifier to categorize inputs, and for the same x_i input above, it gives you:

[0.2 0.3 0.1 0.4]

Averaging the results for the Neural Network and Naive Bayes classifiers, we get:

[((0+0.2)/2) ((0.45+0.3)/2) ((0.55+0.1)/2) ((0+0.4)/2)]

Which yields:

[0.1 0.375 0.325 0.2]

Using this ensemble approach, we classify the input x_i in the second category, whereas each method on their own predicts a different category.

• Thanks redress! Combine output is a good idea, but i want to find the method that has the better result. – Giuseppe Angora Jun 1 '17 at 23:17
• Your question is very unclear, then – redress Jun 1 '17 at 23:39