Default logic is used to describe regular behavior and normal properties. We suggest to exploit the framework of default logic for detecting outliers individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledgebase integrity and to single out irregular individuals. We first formally define the notion of an outlier and ...

OBJECTIVE To determine the effect of spending time as an outlier (ie, an inpatient who spends time away from his or her "home" ward) on the frequency of emergency calls for patients admitted to a tertiary referral hospital. DESIGN, SETTING AND PATIENTS Observational cohort study of all patients admitted to a university-affiliated tertiary referral hospital in Melbourne, Victoria, between 1 Ju...

Outlier detection is an integral part of robust evaluation for crowdsourceable Quality of Experience (QoE) and has attracted much attention in recent years. In QoE for multimedia, outliers happen because of different test conditions, human errors, abnormal variations in context, etc. In this paper, we propose a simple yet effective algorithm for outlier detection and robust QoE evaluation named...

The presence of outliers in time series can seriously affect the model specification and parameter estimation. To avoid these adverse effects, it is essential to detect these outliers and remove them from time series. By the Bayesian statistical theory, this article proposes a method for simultaneously detecting the additive outlier (AO) and innovative outlier (IO) in an autoregressive moving-a...

Context: When working with health-related questionnaires, outlier detection is important. However, traditional methods of outlier detection (e.g., boxplots) can miss participants with "atypical" responses to the questions that otherwise have similar total (subscale) scores. In addition to detecting outliers, it can be of clinical importance to determine the reason for the outlier status or "aty...

Intrusion Detection System (IDS) is a potential part in the area of network security system. An effective intrusion detection system is necessary for providing effective communications in the past world. The major challenging task in this system is the classification of users such as normal user and attacker. For that purpose so many classification algorithms have been proposed in the past to d...

The term “outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous application...

Ignoring two main characteristics of the concentration-response data, correlation between observations and presence of outliers, may lead to misleading results. Therefore the special method should be considered. In this paper in to examine the effect of phenylephrine in rat Corpus cavernosum, outlier robust nonlinear mixed estimation is used. in this study, eight different doses of phenylephrin...

A robust approach should be considered when estimating regression coefficients in multi-response problems. Many models are derived from the least squares method. Because the presence of outlier data is unavoidable in most real cases and because the least squares method is sensitive to these types of points, robust regression approaches appear to be a more reliable and suitable method for addres...

The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. In this article, we present an algorithm that provides outlier detection and data clustering simul...