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POFor classifying data into N classes, is there an alternative to using N yes-no classifiers?
 

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  1. POFor classifying data into N classes, is there an alternative to using N yes-no classifiers?
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    <p>TL;DR: is there any kind of classifier more sophisticated than a yes-no classifier?</p> <p>I'll say up front that I don't have a specific project I'm working on, and this is more of a technique question I've been wondering about.</p> <p>I've worked on a few machine learning applications for one reason or another. All of these projects were intended to classify data into one of N classes, and they all used N yes-no classifiers (if that's what they're called). Each of these classifiers gives a piece of data some score (0 to 1, or -1 to 1) which corresponds to the likelyhood that it's the class that the classifier was trained for. It's then up to the program to use those scores to determine the best classification somehow.</p> <p>I've seen this on both nominal and continuous data, with different implementations of the final classification. For example, I once wrote a small document language identifier in which classifiers were trained on English, French, German, etc, and whichever classifier gave the highest score won. This makes sense to me.</p> <p>Another project classified data on a continuous scale, mostly from 0 to 1.2, but with some data up to 6. We made 6 or so classifiers and assigned them to bins: 0-0.2, 0.2-0.4, ... and 1.0 and above. Once all the classifiers returned for some piece of data, we then fit a quadratic to the scores and took the peak as the result. This makes me uncomfortable, but I don't know why.</p> <p>It seems like there should be a better way than just polling a set of yes-no classifiers and trying to decide based on some algorithm. To take a silly example, consider a system to decide whether a picture shows an onion or a mushroom. (This is literally the first thing I thought of.) I would argue that the more an object looks like an onion, <em>the less it looks like a mushroom</em>, and from an ontological perspective I want a classification method that reflects that. If I have two yes-no classifiers that don't take into account that onionity opposes mushroomness, what do I do about a picture that gets high scores from both? Is there some way to get a single, mushroom-or-onion classifier that somehow <em>knows</em> that there is no overlap between these two classes of vegetation? Or can I count on training the yes-no classifiers with real data to reflect this without any special intervention?</p>
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    1. COOutstanding answers all around. Thanks!
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