Data usage of the function iforest during sampling
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In the built-in function iforest we can set the hyperparameter 'NumObservationsPerLearner', which is the number of observations for each isolation tree.
When a point of the dataset is selected to train an isolation tree, can it be used to train another tree or it is removed from the data that can be used to train the next tree?
If it cannot be used, how does the function behave when 'NumObservationsPerLearner * NumLearners' is greater than the number of point of the given dataset?
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Regarding your main question: "When a point of the dataset is selected to train an isolation tree, can it be used to train another tree?", the short answer is yes, that point (or observation) can be used to train a different tree. The sampling process for each tree begins with the full set of data points/observations.
This can be seen on the doc page: https://www.mathworks.com/help/stats/anomaly-detection-with-isolation-forest.html
- NumObservationsPerLearner (number of observations for each isolation tree) — Each isolation tree corresponds to a subset of training observations. For each tree, iforest samples min(N,256) number of observations from the training data without replacement, where N is the number of training observations. The isolation forest algorithm performs well with a small sample size because it helps to detect dense anomalies and anomalies close to normal points. However, you need to experiment with the sample size if N is small. For an example, see Examine NumObservationsPerLearner for Small Data."
So, given that each tree begins with the full dataset, it is no problem to train an isolation forest of 100 trees with NumObservationPerLearner of 149 using a data set of only 150 observations. See the doc section Examine NumObservationsPerLearner for Small Data for more info.
load fisheriris
size(meas)
[forest,tf,scores]=iforest(meas,NumObservationsPerLearner=149);
forest
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2 个评论
Francesco Bellucci
2023-12-12
I completely agree with your statement, but I would personally correct the previous sentence like this:
For each tree, iforest samples min(N,256) number of observations from the training data without replacement, "if it's possible", where N is the number of training observations.
When sampling without replacement is applied to training data, it means that when creating a smaller training set (or subset) that trains each binary tree, each observation is selected once and not returned to the training data pool.
So, in the case you mentioned before, if you train the IsolationForest model with NumLearners: 100 and NumObservationsPerLearner: 149 without replacement, you will get a total of 14.900 samples.
The dataset under consideration contains only 150 observations, so sampling without replacement is impossible.
Drew
2023-12-12
The sampling from the training data is reset for every tree. That is why the number of samples per tree is bounded by the lower of N or 256. The phrase "without replacement" applies to the sampling process for one tree. When the sampling process begins for the next tree, the process starts over with all of the training samples.
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