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IsolationForest

Isolation forest for anomaly detection

    Description

    Use an isolation forest (ensemble of isolation trees) model object IsolationForest for outlier detection and novelty detection.

    • Outlier detection (detecting anomalies in training data) — Detect anomalies in training data by using the iforest function. The iforest function builds an IsolationForest object and returns anomaly indicators and scores for the training data.

    • Novelty detection (detecting anomalies in new data with uncontaminated training data) — Create an IsolationForest object by passing uncontaminated training data (data with no outliers) to iforest, and detect anomalies in new data by passing the object and the new data to the object function isanomaly. The isanomaly function returns anomaly indicators and scores for the new data.

    Creation

    Create an IsolationForest object by using iforest.

    Properties

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    This property is read-only.

    Categorical predictor indices, specified as a vector of positive integers. CategoricalPredictors contains index values indicating that the corresponding predictors are categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If none of the predictors are categorical, then this property is empty ([]).

    This property is read-only.

    Fraction of anomalies in the training data, specified as a numeric scalar between 0 and 1.

    • If the ContaminationFraction value is 0, then iforest treats all training observations as normal observations, and sets the score threshold (ScoreThreshold property value) to the maximum anomaly score value of the training data.

    • If the ContaminationFraction value is in the range (0,1], then iforest determines the threshold value (ScoreThreshold property value) so that the function detects the specified fraction of training observations as anomalies.

    This property is read-only.

    Number of isolation trees, specified as a positive integer scalar.

    This property is read-only.

    Number of observations to draw from the training data without replacement for each isolation tree, specified as a positive integer scalar.

    This property is read-only.

    Predictor variable names, specified as a cell array of character vectors. The order of the elements of PredictorNames corresponds to the order in which the predictor names appear in the training data.

    Data Types: cell

    This property is read-only.

    Threshold for the anomaly score used to identify anomalies in the training data, specified as a numeric scalar between 0 and 1.

    The software identifies observations with anomaly scores above the threshold as anomalies.

    • The iforest function determines the threshold value to detect the specified fraction (ContaminationFraction property) of training observations as anomalies.

    • The isanomaly object function uses the ScoreThreshold property value as the default value of the ScoreThreshold name-value argument.

    Object Functions

    isanomalyFind anomalies in data using isolation forest

    Examples

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    Detect outliers (anomalies in training data) by using the iforest function.

    Load the sample data set NYCHousing2015.

    load NYCHousing2015

    The data set includes 10 variables with information on the sales of properties in New York City in 2015. Print a summary of the data set

    summary(NYCHousing2015)
    Variables:
    
        BOROUGH: 91446x1 double
    
            Values:
    
                Min          1    
                Median       3    
                Max          5    
    
        NEIGHBORHOOD: 91446x1 cell array of character vectors
    
        BUILDINGCLASSCATEGORY: 91446x1 cell array of character vectors
    
        RESIDENTIALUNITS: 91446x1 double
    
            Values:
    
                Min            0  
                Median         1  
                Max         8759  
    
        COMMERCIALUNITS: 91446x1 double
    
            Values:
    
                Min           0   
                Median        0   
                Max         612   
    
        LANDSQUAREFEET: 91446x1 double
    
            Values:
    
                Min                0
                Median          1700
                Max       2.9306e+07
    
        GROSSSQUAREFEET: 91446x1 double
    
            Values:
    
                Min                0
                Median          1056
                Max       8.9422e+06
    
        YEARBUILT: 91446x1 double
    
            Values:
    
                Min            0  
                Median      1939  
                Max         2016  
    
        SALEPRICE: 91446x1 double
    
            Values:
    
                Min                0
                Median    3.3333e+05
                Max       4.1111e+09
    
        SALEDATE: 91446x1 datetime
    
            Values:
    
                Min       01-Jan-2015
                Median    09-Jul-2015
                Max       31-Dec-2015
    

    The SALEDATE column is a datetime array, which is not supported by iforest. Create columns for the month and day numbers of the datetime values, and delete the SALEDATE column.

    [~,NYCHousing2015.MM,NYCHousing2015.DD] = ymd(NYCHousing2015.SALEDATE);
    NYCHousing2015.SALEDATE = [];

    The columns BOROUGH, NEIGHBORHOOD, and BUILDINGCLASSCATEGORY contain categorical predictors. Display the number of categories for the categorical predictors.

    length(unique(NYCHousing2015.BOROUGH))
    ans = 5
    
    length(unique(NYCHousing2015.NEIGHBORHOOD))
    ans = 254
    
    length(unique(NYCHousing2015.BUILDINGCLASSCATEGORY))
    ans = 48
    

    For a categorical variable with more than 64 categories, the iforest function uses an approximate splitting method that can reduce the accuracy of the isolation forest model. Remove the NEIGHBORHOOD column, which contains a categorical variable with 254 categories.

    NYCHousing2015.NEIGHBORHOOD = [];

    Train an isolation forest model for NYCHousing2015. Specify the fraction of anomalies in the training observations as 0.1, and specify the first variable (BOROUGH) as a categorical predictor. The first variable is a numeric array, so iforest assumes it is a continuous variable unless you specify the variable as a categorical variable.

    rng("default") % For reproducibility 
    [Mdl,tf,scores] = iforest(NYCHousing2015,ContaminationFraction=0.1, ...
        CategoricalPredictors=1);

    Mdl is an IsolationForest object. iforest also returns the anomaly indicators (tf) and anomaly scores (scores) for the training data NYCHousing2015.

    Plot a histogram of the score values. Create a vertical line at the score threshold corresponding to the specified fraction.

    histogram(scores)
    xline(Mdl.ScoreThreshold,"r-",["Threshold" Mdl.ScoreThreshold])

    Figure contains an axes object. The axes object contains 2 objects of type histogram, constantline.

    If you want to identify anomalies with a different contamination fraction (for example, 0.01), you can retrain an isolation forest model.

    rng("default") % For reproducibility 
    [newMdl,newtf,scores] = iforest(NYCHousing2015, ...
        ContaminationFraction=0.01,CategoricalPredictors=1);
    

    If you want to identify anomalies with a different score threshold value (for example, 0.65), you can pass the IsolationForest object, the training data, and a new threshold value to the isanomaly function.

    [newtf,scores] = isanomaly(Mdl,NYCHousing2015,ScoreThreshold=0.65);
    

    Note that changing the contamination fraction or score threshold does not change the anomaly scores. Therefore, if you do not want to compute the anomaly scores again by using iforest or isanomaly, you can obtain a new anomaly identifier with the existing score values.

    Change the fraction of anomalies in the training data to 0.01.

    newContaminationFraction = 0.01;

    Find a new score threshold by using the quantile function.

    newScoreThreshold = quantile(scores,1-newContaminationFraction)
    newScoreThreshold = 0.6597
    

    Obtain a new anomaly identifier.

    newtf = scores > newScoreThreshold;

    Create an IsolationForest object for uncontaminated training observations by using the iforest function. Then detect novelties (anomalies in new data) by passing the object and the new data to the object function isanomaly.

    Load the 1994 census data stored in census1994.mat. The data set consists of demographic data from the US Census Bureau to predict whether an individual makes over $50,000 per year.

    load census1994

    census1994 contains the training data set adultdata and the test data set adulttest.

    Train an isolation forest model for adultdata. Assume that adultdata does not contain outliers.

    rng("default") % For reproducibility
    [Mdl,tf,s] = iforest(adultdata);

    Mdl is an IsolationForest object. iforest also returns the anomaly indicator tf and anomaly scores s for the training data adultdata. If you do not specify the ContaminationFraction name-value argument as a value greater than 0, then iforest treats all training observations as normal observations, that is, the values in tf are all logical 0 (false). The function sets the score threshold to the maximum score value. Display the threshold value.

    Mdl.ScoreThreshold
    ans = 0.8600
    

    Use the trained isolation forest model to find anomalies in adulttest.

    [tf_test,s_test] = isanomaly(Mdl,adulttest);

    The isanomaly function returns anomaly indicators tf_test and scores s_test for adulttest. By default, isanomaly identifies observations with scores above the threshold (Mdl.ScoreThreshold) as anomalies.

    Create histograms for anomaly scores s and s_test. Create a vertical line at the threshold of the anomaly scores.

    histogram(s,Normalization="probability")
    hold on
    histogram(s_test,Normalization="probability")
    xline(Mdl.ScoreThreshold,"r-",join(["Threshold" Mdl.ScoreThreshold]))
    legend("Training Data","Test Data",Location="northwest")
    hold off

    Figure contains an axes object. The axes object contains 3 objects of type histogram, constantline. These objects represent Training Data, Test Data.

    Display the observation index of the anomalies in the test data.

    find(tf_test)
    ans = 15655
    

    The anomaly score distribution of the test data is similar to that of the training data, so isanomaly detects a small number of anomalies in the test data with the default threshold value. You can specify a different threshold value by using the ScoreThreshold name-value argument. For an example, see Specify Anomaly Score Threshold.

    More About

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    References

    [1] Liu, F. T., K. M. Ting, and Z. Zhou. "Isolation Forest," 2008 Eighth IEEE International Conference on Data Mining. Pisa, Italy, 2008, pp. 413-422.

    Introduced in R2021b