We propose thresholding as an approach to deal with class imbalance. We define the concept of thresholding as a process of determining a decision boundary in the presence of a tunable parameter. The threshold is the maximum value of this tunable parameter where the conditions of a certain decision are satisfied. We show that thresholding is applicable not only for linear classifiers but also fo...

In sensor network, collected data is error prone due to errors during sensors and transmission. Sometimes, the sensed data may appear to be erroneous due to large deviation from normal data distribution. Such data points termed as outliers may contain some important pattern. Outliers, if neglected as erroneous data, may result in failure to detect important phenomenon. Hence, it is necessary to...

Inference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is method that heavily relies on probabilistic inference. This allows outstanding sample efficiency because the probabilistic machinery provides a memory of the whole optimization process. However, that virtue becomes a ...

The performance of model-based engine calibration is highly dependent on the type of modelling which is used. A problem for state of the art algorithms for engine calibration arises, if outliers occur in the measurement data. Since outliers are not considered in recent types of modelling for engine calibration, they have to be removed before model training, in order to get a good model quality ...

Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density-based point cloud denoising method is presented to remove outliers and noisy points. First, particle-swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust perf...

The paper presents a robust version of a recent anisotropic orthogonal Procrustes algorithm that has been proposed to solve the socalled camera exterior orientation problem in computer vision and photogrammetry. In order to identify outliers, that are common in visual data, we propose an algorithm based on Least Median of Squares to detect a minimal outliers-free sample, and a Forward Search pr...

Detecting outliers is an important task for many applications including fraud detection or consistency validation in real world data. Particularly in the presence of uncertain data or imprecise data, similar objects regularly deviate in their attribute values. The notion of outliers has thus to be defined carefully. When considering outlier detection as a task which is complementary to clusteri...

The increasing availability of detailed inpatient data is enabling the development of data-driven approaches to provide novel insights for the management of Length of Stay (LOS), an important quality metric in hospitals. This study examines clustering of inpatients using clinical and demographic attributes to identify LOS outliers and investigates the opportunity to reduce their LOS by comparin...

Numerous applications in dynamic social networks, ranging from telecommunications to financial transactions, create evolving datasets. Detecting outliers in such dynamic networks is inherently challenging, because the arbitrary linkage structure with massive information is changing over time. Little research has been done on detecting outliers for dynamic social networks, even then, they repres...

Outlier detection is an important problem that has applications in many fields. High dimensional datasets are common in such applications. Among the existing outlier detection methods, Distance-Based outlier (DB-Outlier) detection is one of the most generalizable and simplest approaches. It finds outliers by calculating distances between data points. However, in high dimensional space, data dis...