CrashOutlier

CrashOutlier provides different outlier detection algorithms in detection task. These outlier detection algorithms include: Statistical-based(Gauss-based), Distance-based(HilOut), Density-based(LOF), Cluster-based(DBSCAN), High dimensional techniques(Isolation Forest).

This project is developed on top of Weka, written in Java, built in Maven. So directly import it into your eclipse maybe a good choice.

1. Usage Example

We have implemented outlier detection algorithm in each Java class, we just need instantiate a object and invoke its corresponding showResults() function to show the detection results, here is the code to invoke Gauss-Based outlier detection to detect outliers,

GaussBased gb = new GaussBased("XXX.arff");
gb.showResults();

2. Detection Output

In the original version of our project, we only output the anomaly score (vary from one algorithm to another) and class label of each instance. Maybe we can add some evaluation measure in the future. And here is the sample output,

Experiments Results of <XXX.arff> By Using Gauss-Based Outlier Detection Method.

---------------- Detected Outliers ------------------

probability: 1.001344193860821E-151, Label: difficult
probability: 1.001344193860821E-151, Label: difficult
probability: 2.87218414628287E-151, Label: difficult
probability: 2.5188243831817195E-150, Label: difficult
probability: 4.1834795368155787E-119, Label: easy
probability: 3.1286018242800934E-109, Label: easy
...
  
---------------- Detected Normals ------------------

probability: 1.4089300821916898E-46, Label: easy
probability: 3.705113213490469E-44, Label: easy
...
----------------------------------
TP:11
TN:169
FP:9
FN:11
PRECISION:0.55
RECALL:0.5
F-MEASURE:0.5238095238095238

3. Limitation

We can only load or read datasets in arff or csv formats just now. In addition, the test suite is also under editing.

arff format:

@relation Jsqlparser

@attribute featureNameI  value numeric
@attribute featureNameII value numeric
...
@data
XX, XX, XX, XX, ..., flagA
XX, XX, XX, XX, ..., flagB
...

csv format:

featureNameI, featureNameII, ..., flag
XX, XX, ..., flagA
XX, XX, .., flagB
...