What does ABOD mean in UNCLASSIFIED

Angle Based Outlier Detection (ABOD) is an outlier detection algorithm used to identify outliers from a given dataset using angles as the main criterion for evaluation. It works by measuring the angles between each sample point and its nearest neighbors. Points that have a larger angle than the average are considered potential outliers.

ABOD works by first computing all pairwise angles between each sample in the dataset, then calculating an average of those angles. Points that have an angle larger than the average angle are seen as potential outliers and flagged accordingly.

ABOD can be applied to any type of numerical or multi-dimensional data set, such as time series information or other kinds of structured data sets.

Unlike many outlier detection algorithms, ABOD is relatively robust when faced with datasets that contain outliers with different values from normal points. The algorithm does not require parameters to be adjusted or tuned, which also makes it easy to use. Additionally, ABOD can handle large numbers of variables quite well and is computationally efficient.

ABOD can be used whenever you need to detect outliers in a dataset quickly and reliably without relying on manually set parameters or thresholds for evaluation. It's particularly useful when faced with large datasets or datasets containing low density regions which may confuse more traditional methods such as K-Nearest Neighbors (KNN).

Choosing too few neighbors could result in false positives (unreliable results), while choosing too many neighbors will reduce the quality/precision of results since it will include points outside the target region in its calculations. In general, you should aim for a small value not exceeding five neighbors if your dataset has more than 1000 samples or 10 if there are 500 samples or fewer.

Yes, highly skewed data sets containing extreme outliers are not well suited for analysis via ABOD as these types of data can produce unreliable results due to their extreme nature skewing the average angle calculations.

On average, most analyses take less than one minute due to its efficient computational requirements - however this can depend on dataset size and complexity.

No, additional time spent analyzing your data does not increase accuracy since all necessary computation has been performed by the algorithm already and no additional changes will affect performance.