Solved – Combining one class classifiers to do multi-class classification

Question

I am working on a 3-class classification problem. The classifier I'm using is Bayesian Networks which provides me with a classification accuracy of around 60%. When I do a two-class classification, I get 80% accuracy for differentiating between class 0 & class 1 and between class 0 & class 2. Also, I get only 60% accuracy for classification between class 1 & class 2. I believe the best way to do 3-class classification in this case would be combining the 2 two-class classifiers with 80% accuracy. What comes to my mind is using some sort weighted averaging scheme on the results of the two individual two-class classifiers. I have not solved such a problem in the past and am facing a dilemma as to how I should implement this. Any help/suggestions in this regard would be highly appreciated. Please fell free to suggest other alternatives if you think they may work.

Answer 1

I've done something like this using either of the following:

1. (a) Given three different classes (e.g. A, B, C), create an input column for each class. Place '1' in the A column if the sample is an A, '0' otherwise - do this for B and C classes using the same logic. The foregoing columns will be your target fields for three separate binary classifiers (a classifier for A, B, and C).

(b) Feed the predictions - in addition to any other features - into a third classifier, a multiclass classifier whose target is the tri-level target.

2. Taking the same approach as 1(a), take the predictions and use rule-based logic (or misclassification costs) to separate the class predictions - this is to avoid ending up with the same sample being predicted as both A and B, both A and C, etc.