Improving Accuracy of Rare Classes in Machine Learning Classifiers

Eylon Caplan Author

08/03/2020 Added

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This video summarizes my work and findings in simple machine learning models and in techniques that improve the classification of rare classes.

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[00:00:00.000]Hi, my name is Eylon Caplan
[00:00:04.178]and I did my UCARE project
[00:00:06.710]on machine learning
[00:00:08.201]and specifically the problem that we
[00:00:10.411]tackled was improving the accuracy of
[00:00:12.861]certain rare classes in machine learning
[00:00:16.274]classification.
[00:00:18.053]And I did my project with Dr. Hasan.
[00:00:22.700]So I'll first introduce machine learning
[00:00:24.971]in general. So, the classifiers that we're
[00:00:27.947]talking about are some sort of algorithm
[00:00:30.018]that is able to take as input a large
[00:00:32.823]training set of data. And then, by some
[00:00:36.745]means, learn to distinguish between
[00:00:39.740]different classes. And then given a
[00:00:41.958]certain input, it's supposed to be able
[00:00:45.111]to classify that into one of the classes,
[00:00:47.705]hopefully with a high accuracy. And so,
[00:00:51.226]specifically, we were talking about
[00:00:53.269]image classification, but the problem
[00:00:55.754]can be expanded to any classification,
[00:00:58.933]really. So one goal was just to explore
[00:01:04.092]different models, find out their strengths
[00:01:06.842]and weaknesses, find ways to evaluate how
[00:01:09.271]good a model is, and then, of course, to
[00:01:12.021]improve the accuracy of certain classes
[00:01:14.593]that are rare during training. So I first
[00:01:18.705]started out with some simple regression,
[00:01:20.834]which is one of the simplest forms of
[00:01:22.757]machine learning. The goal in regression
[00:01:24.973]is just to find a continuous value. So in
[00:01:28.289]this case, we used the California dataset,
[00:01:30.935]which is a dataset that has all these
[00:01:33.539]statistics about housing, including the
[00:01:37.681]target value, which is median house value.
[00:01:39.825]And then the model was fed this
[00:01:42.086]information and then tried to guess the
[00:01:44.603]median house value of certain districts
[00:01:47.751]or regions in California. As you can see,
[00:01:51.654]because it's in reality a pretty
[00:01:54.093]complicated model, the linear regression-
[00:01:56.476]type model was underfitting, but I did
[00:01:59.657]learn quite a bit about being able to use
[00:02:02.838]this in Python and quite a bit about
[00:02:05.237]visualizing the data
[00:02:07.101]in order to figure
[00:02:08.024]out what kind of model you want to
[00:02:09.672]fit it with. The main dataset that I used
[00:02:13.854]throughout the summer was the wine
[00:02:17.225]quality dataset, which is about 5,000
[00:02:20.876]wines which were chemically analyzed
[00:02:24.865]with all of the things that you can see
[00:02:26.721]in front of you, along with the quality,
[00:02:29.337]which was determined by some
[00:02:31.484]professional wine tasters. And the goal
[00:02:35.684]was to turn this into a binary
[00:02:38.993]classification--so either good wine or
[00:02:41.007]bad wine. And the way I did that was I
[00:02:43.019]just used some threshold of the quality,
[00:02:45.276]which was originally I think 1-7. And so I
[00:02:48.284]was able to turn it into either good or
[00:02:50.307]bad wine. As you can see the ratio of good
[00:02:54.113]wines was higher than the ratio of bad
[00:02:56.269]wines, which is something that enabled me
[00:02:59.611]to use this dataset as sort of like an
[00:03:02.022]analogy to rare classification. Again I
[00:03:07.879]started here with logistic regression.
[00:03:09.977]This is like the linear regression except
[00:03:13.857]it is for classification. And in this
[00:03:17.210]case, as you can see with two features,
[00:03:19.401]it simply draws a line that maximizes
[00:03:22.817]the classes' difference--separation. And
[00:03:26.102]in this case it doesn't perform very well
[00:03:29.102]because 0.668 is barely above, or even
[00:03:34.973]below just guessing randomly every time.
[00:03:38.726]But still, you can use this visualization
[00:03:41.092]technique to see whether it would even
[00:03:43.166]be possible to draw something that could
[00:03:45.206]distinguish between the two classes. This
[00:03:49.559]is logistic regression. Again, this is
[00:03:51.878]using two different features from before.
[00:03:53.900]And these ones, as you can see, are more
[00:03:55.802]separable than the one before it. But
[00:03:57.571]still, you don't have as good accuracy.
[00:04:00.379]You still have a higher accuracy than
[00:04:02.320]guessing randomly every time, which is
[00:04:05.054]better. But also something that you can
[00:04:07.759]see is that you can use two features
[00:04:09.754]individually, but also you can use all of
[00:04:11.959]the features. So there were about 12
[00:04:13.762]features in the wine quality dataset. And
[00:04:16.490]using all of the features, there might be
[00:04:18.629]some more complex way to classify the good
[00:04:24.440]vs. bad wine, but it's a lot harder to
[00:04:26.777]visualized because we have that
[00:04:29.040]dimensionality problem with being unable
[00:04:31.437]to graph all of the features at once. So
[00:04:33.743]sometimes it was useful to graph two
[00:04:35.771]features at once, even though they weren't
[00:04:37.811]totally separable. Then thing we tried was
[00:04:42.682]polynomial regression, which is actually
[00:04:44.939]the same thing as logistic regression
[00:04:46.790]except it adds some polynomial
[00:04:49.539]combinations of the features, which is
[00:04:52.824]really expensive and grows really quickly
[00:04:55.220]if you have a lot of features. So I really
[00:04:59.020]only did this with a few features at a
[00:05:01.694]time and as you can see this is the same
[00:05:03.928]set of features from before and it does
[00:05:06.118]fit it a little bit better, but still
[00:05:08.997]there is a lot of overlap here and it's
[00:05:11.036]very difficult to find separation with
[00:05:12.860]these kinds of models. The next one that
[00:05:17.406]we tried is a really powerful technique
[00:05:19.669]called KNN, which is the K-nearest neigbor
[00:05:21.720]And this is a very simple idea but it can
[00:05:25.523]fit very complex data and the way that it
[00:05:28.452]does it is it just mimics what its
[00:05:30.410]neighbors are doing. So you pick a data
[00:05:32.378]point and it'll look around at its
[00:05:34.106]neighbors and form some sort of
[00:05:36.157]consensus and decide what's more likely.
[00:05:39.149]The hyperparameters there are how you
[00:05:42.714]classify distance between the points, and
[00:05:44.970]also how many points you're looking
[00:05:46.797]at around where the specific input is.
[00:05:50.796]But you can see here there is some severe
[00:05:53.526][overfitting], because what you have here
[00:05:55.710]is a high training accuracy and a lower
[00:05:58.190]test accuracy, which means that it's not
[00:06:02.125]generalizing well. But still this has been
[00:06:06.605]the highest accuracy that we'd gotten so
[00:06:09.771]far. The next one was a support vector
[00:06:12.373]machine. This one is also capable of very
[00:06:16.738]complex classification.
[00:06:20.161]And it also had very
[00:06:21.161]high results. Again, I always did one
[00:06:23.227]feature vs. another and then all of the
[00:06:25.091]features at once because one vs. another
[00:06:27.444]helps you see how it's distinguishing; if
[00:06:30.905]it's doing it properly. And using all of
[00:06:33.568]the features might compensate for more
[00:06:35.709]complex models--ways of classifying it.
[00:06:42.456]And the good thing about the support
[00:06:45.825]vector machine is that it's much, much
[00:06:48.520]cheaper, than say polynomial regression,
[00:06:52.160]but it can fit very complicated models
[00:06:55.794]here. This one also had, I think,
[00:06:58.708]the highest [accuracy] of the models that
[00:07:00.761]we had until that point.
[00:07:03.664]Here we tried a bunch of different
[00:07:06.350]balancing techniques, because there were
[00:07:08.264]more good wines than there were bad wines,
[00:07:10.463]and so trying to compensate for that there
[00:07:12.491]were a few techniques. So one of them was
[00:07:14.513]just weighting classes, which is just
[00:07:16.309]adding more importance to the rarer
[00:07:18.188]classes. But this doesn't perform very
[00:07:20.687]well. And it didn't perform very well for
[00:07:23.345]us either. And then there is
[00:07:25.260]undersampling and oversampling, which is
[00:07:27.176]just feeding fewer examples of the
[00:07:29.122]overrepresented class and oversampling
[00:07:32.130]which is the opposite. And then there was
[00:07:34.526]also an unsupervised learning method
[00:07:37.103]called one class SVM, which tries to
[00:07:38.922]detect outliers, but this method is mostly
[00:07:41.650]effective if classification can be made
[00:07:47.734]via determining outliers, which isn't
[00:07:49.996]always the case. So we see here with
[00:07:54.610]sampling and no sampling, the accuracy
[00:07:57.790]went down when we tried to do the sampling
[00:08:00.690]But the interesting thing was that the
[00:08:02.866]precision and recall tradeoff, which are
[00:08:05.029]two measures of the types of errors that
[00:08:07.109]are being made, could be more equalized
[00:08:09.090]using these techniques. So with no
[00:08:11.590]sampling, there in the middle, you see
[00:08:14.295]that it has a very high recall but a much
[00:08:16.562]lower precision. And with both of the
[00:08:18.467]sampling techniques, you see that those
[00:08:20.467]recall and precision got a little closer
[00:08:22.406]to each other, which is often the thing
[00:08:24.294]that you want to do when trying to solve
[00:08:26.853]this rare classification problem.
[00:08:29.605]This is just a list of the models that we
[00:08:31.570]tried. I didn't go over the rest of them
[00:08:33.761]because there wasn't too much time, but
[00:08:35.806]the random forest, boosting and bagging,
[00:08:38.077]and the ensemble classifier were also done
[00:08:40.606]The idea of this project was to utilize
[00:08:46.046]these techniques that I tried and to also
[00:08:48.827]build a basis for these machine learning
[00:08:53.505]models and also the techniques being used
[00:08:55.691]here. The idea would be to use it on the
[00:09:00.735]Snapshot Serengeti dataset, which is a
[00:09:03.645]dataset of camera-trap images of animals
[00:09:08.337]taken and the goal would be to classify
[00:09:10.940]animals and also what action the animals
[00:09:15.226]are doing--if they're sleeping or if
[00:09:18.792]they're eating or drinking or something
[00:09:20.923]like that. And as you can see in this
[00:09:23.760]graph there is a huge imbalance in the
[00:09:26.095]dataset; these are of pictures with
[00:09:28.125]animals in them. Zebras and wildebeest
[00:09:31.212]make up over 50% of the pictures,
[00:09:35.310]which means that these less common
[00:09:38.445]animals are much more difficult to
[00:09:40.325]classify and have higher rates of error.
[00:09:42.753]So the goal would be to utilize the
[00:09:45.929]techniques that I studied in order to
[00:09:48.341]improve the accuracy--or precision-recall
[00:09:51.712]tradeoff in this dataset. Thank you.

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Improving Accuracy of Rare Classes in Machine Learning Classifiers

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