I have a training set with about 3000 positive instances and 3000 negative instances. But my test data set is pretty much un-balanced. The positive set only has 50 instances and negative has 1500 instances.This causes the precision very low. Are there any approaches to solve this problem? I use SVM to build classifier.
Machine Learning – Handling Highly Unbalanced Test Data Set and Balanced Training Data in Classification
classificationdata miningmachine learningsvm
Related Solutions
This is an interesting and very frequent problem in classification - not just in decision trees but in virtually all classification algorithms.
As you found empirically, a training set consisting of different numbers of representatives from either class may result in a classifier that is biased towards the majority class. When applied to a test set that is similarly imbalanced, this classifier yields an optimistic accuracy estimate. In an extreme case, the classifier might assign every single test case to the majority class, thereby achieving an accuracy equal to the proportion of test cases belonging to the majority class. This is a well-known phenomenon in binary classification (and it extends naturally to multi-class settings).
This is an important issue, because an imbalanced dataset may lead to inflated performance estimates. This in turn may lead to false conclusions about the significance with which the algorithm has performed better than chance.
The machine-learning literature on this topic has essentially developed three solution strategies.
You can restore balance on the training set by undersampling the large class or by oversampling the small class, to prevent bias from arising in the first place.
Alternatively, you can modify the costs of misclassification, as noted in a previous response, again to prevent bias.
An additional safeguard is to replace the accuracy by the so-called balanced accuracy. It is defined as the arithmetic mean of the class-specific accuracies, $\phi := \frac{1}{2}\left(\pi^+ + \pi^-\right),$ where $\pi^+$ and $\pi^-$ represent the accuracy obtained on positive and negative examples, respectively. If the classifier performs equally well on either class, this term reduces to the conventional accuracy (i.e., the number of correct predictions divided by the total number of predictions). In contrast, if the conventional accuracy is above chance only because the classifier takes advantage of an imbalanced test set, then the balanced accuracy, as appropriate, will drop to chance (see sketch below).
I would recommend to consider at least two of the above approaches in conjunction. For example, you could oversample your minority class to prevent your classifier from acquiring a bias in favour the majority class. Following this, when evaluating the performance of your classifier, you could replace the accuracy by the balanced accuracy. The two approaches are complementary. When applied together, they should help you both prevent your original problem and avoid false conclusions following from it.
I would be happy to post some additional references to the literature if you would like to follow up on this.
From a recent post on reddit, the reply by datapraxis will be of interest.
edit: the paper mentioned is Haibo He, Edwardo A. Garcia, "Learning from Imbalanced Data," IEEE Transactions on Knowledge and Data Engineering, pp. 1263-1284, September, 2009 (PDF)
Best Answer
This is called Dataset Shift setting. This pdf [1] should help you understand several of the underlying issues involved.
For the moment however, you can use least squares importance fitting to obtain importance estimates for your training data using your test set (you don't need the test set labels, just the feature vectors) [2]. Once you gain the importance estimates, you can use them as instance weights in libSVM [3].
That should enable you to get a better classifier.
[1] http://www.acad.bg/ebook/ml/The.MIT.Press.Dataset.Shift.in.Machine.Learning.Feb.2009.eBook-DDU.pdf
[2] http://www.ms.k.u-tokyo.ac.jp/software.html#uLSIF
[3] http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances