MATLAB: Regarding usage of classifier in case of imbalance data

classificationComputer Vision ToolboxdatasetImage Processing ToolboximbalancedStatistics and Machine Learning Toolboxunbalance

I have images database consisting of four classes, but highly imbalanced. One class contains 150 images other 1500, 1800,1000. which classifier can be used to get efficient results for classification. How can i divide these images into training and testing. Kindly help

Best Answer

You can use randsample() or randperm() to get a list of 120 (80% of the smallest group) from each of the 4 groups. Use those 480 images for training, and the rest for testing/validation of the model.

Related Solutions

MATLAB: Handling imbalanced data with patternnet

Hi,

To weight the errors during training, one can specify the "error weights" (EW) property in 'train', see here:

https://www.mathworks.com/help/deeplearning/ref/train.html#d117e35592

At prediction time, one can pass the error weights to the performance function, either by calling 'perform' with 'ew' or 'crossentropy' with 'perfWeights'.

For example (to weight the classes in the Iris dataset by 0.1, 0.2 and 0.7):

[x,t] = iris_dataset;
net = patternnet(10);
net = train(net,x,t,[],[],[0.1,0.2,0.7]');
y = net(x);
perf = perform(net,t,y,[0.1,0.2,0.7]')

Or:

perf = crossentropy(net,t,y,[0.1,0.2,0.7]')

Hope this helps!

MATLAB: Dealing imbalanced data in neural network

There many ways to deal with unbalanced classes when there is no more real data available. Over the decades I have used the following

 1. Use the summary statistics of small classes to simulate more data
 2. Design multiple nets using the smaller classes and subsets of the larger classes. 
    Then combine the answers.
 3. Use a cost matrix to enhance the influence of the small subsets 
    and/or reduce the influence of the larger subsets
 4. A combination of the above.

The basis of the techniques can be understood by examining the following term in the Bayesian Risk

 Cij * Pi * p(i|x)

which involves the probability density, a prori probability and the classification cost.

Hope this helps.

Thank you for formally accepting my answer

Greg

Best Answer

Related Solutions

MATLAB: Handling imbalanced data with patternnet

MATLAB: Dealing imbalanced data in neural network

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