How to implement incremental random forest in matlab?

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乐乐
乐乐 2023-7-4
编辑： Sandeep 2023-7-27
I would like to ask whether the online learning of incremental random forest can be realized by adding a decision tree trained by new samples to the random forest classifier in matlab. At present, I only know that matlab can realize the incremental learning function of linear models.
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Kautuk Raj
Kautuk Raj 2023-7-4
One approach to achieve incremental learning in random forests is to use the online random forest algorithm, which incrementally updates the forest with new data. The online random forest algorithm works by adding new decision trees to the forest as new data becomes available, and then updating the weights of the existing trees based on how well they classify the new data.
乐乐
乐乐 2023-7-5
Hello, thank you for your reply. I would like to ask you how to add a decision tree trained by a new sample to the random forest in matlab, I see that matlab seems to only support the incremental learning of linear models, for the non-linear random forest does not directly provide a method to increase the tree to the 'TreeBagger' object, would like to ask you how this should be implemented? thank you.

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Sandeep
Sandeep 2023-7-27
编辑：Sandeep 2023-7-27
Hi 乐乐,
It is my understanding that you want to know how to add a decision tree trained by a new sample to the random forest in MATLAB.
  1. Train a new decision tree using the new sample data.
  2. You can use the fitctree function in MATLAB to train a decision tree classifier.
  3. Retrieve the existing random forest model rfModel, using the TreeBagger object.
  4. Access the individual decision trees within the random forest using the rfModel.Trees property. This property returns a cell array of trained decision trees.
  5. Append the new decision tree to the existing random forest by adding it to the existingTrees cell array.
  6. Update the rfModel.Trees property with the modified existingTrees cell array.
  7. Now, your random forest model rfModel will include the newly trained decision tree. You can use the updated model for prediction or further analysis.
A Sample implementation is given below,
newTree = fitctree(newSampleData, newSampleLabels);
rfModel = TreeBagger(numTrees, trainingData, trainingLabels);
existingTrees = rfModel.Trees;
existingTrees{end+1} = newTree;
rfModel.Trees = existingTrees;
Refer the Documentation page of TreeBagger for more information.

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