Neighborhood Averaging for Improving Outlier Detectors

-- We hypothesize that similar objects should have similar outlier scores. To our knowledge, all existing outlier detectors calculate the outlier score for each object independently regardless of the outlier scores of the other objects. Therefore, they do not guarantee that similar objects have similar outlier scores. To verify our proposed hypothesis, we propose an outlier score post-processing technique for outlier detectors, called neighborhood averaging (NA), which pays attention to objects and their neighbors and guarantees them to have more similar outlier scores than their original scores. Given an object and its outlier score from any outlier detector, NA modifies its outlier score by combining it with its k nearest neighbors' scores. We demonstrate the effectivity of NA by using the well-known k-nearest neighbors (k-NN). Experimental results show that NA improves all 10 tested baseline detectors by 13% (from 0.70 to 0.79 AUC) on average evaluated on nine real-world datasets. Moreover, even outlier detectors that are already based on k-NN are also improved. The experiments also show that in some applications, the choice of detector is no more significant when detectors are jointly used with NA, which may pose a challenge to the generally considered idea that the data model is the most important factor. Outliers are objects that significantly deviate from other objects. Outliers can indicate useful information, which can be applied in applications such as fraud detection [1, 2], abnormal time series [3, 4], and traffic patterns [5, 6]. Outliers can also be harmful because they are generally unwanted, can be considered errors, and may have biased statistical analysis for applications like clustering [7, 8]. Recently, outlier detection has also been applied to manufacturing data [9] and industrial applications [10]. For these reasons, outliers need to be detected. Most outlier detectors calculate the so-called outlier score for every object independently and then calculate the threshold scores that deviate significantly from the others and label them as outliers [11].