I found this relevant information – An Overview of Ensemble Methods for Binary Classifiers in Multi-class Problems: Experimental Study on One-vs-One and One-vs-All Schemes

The following problem is pertaining to Text Classification.

Problem: Let’s say I have a text dataset which I want to bifurcate into classes A, B, C, and Others. Around 60% of the text is going to be Others (Non A, B, C class) and the rest 40% is distributed among A,B and C, not mutually exclusive! That is, a sentence can belong to class A & B or A & C and so on.

Further the annotated dataset is in the form of three CSVs – A vs ~A, B vs ~B and C vs ~C. Note that the sentences belonging to ~<Class_Name> in each of the CSVs is always large.

Initially the problem has been treated as a multiple classifications – have been built in the form of A vs others, B vs others and C vs others.

At this juncture, where the three binary classifiers are already built and which are working ok-ish right now (as per requirements) –

  1. How would you suggest to combine the classifiers into one multi-class ? That is where the linked resource comes in. Multi-label would be one step ahead.

  2. The second alternative could be merge the different CSVs into one CSV and start building models based on this aggregated which would ultimately lead to building a multi-class classifier. But still the problem of multi-label remains. I have no idea how that should be approached.

Has anyone encountered such a situation before? Appreciate the help 🙂

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( https://www.reddit.com/r//comments/984b57/d__multiple_binary_classifiers_into_a/)


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