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...

‘‘One person’s noise is another person’s signal” (Knorr, E., Ng, R. (1998). Algorithms for mining distancebased outliers in large datasets. In Proceedings of the 24th VLDB conference, New York (pp. 392–403)). In recent years, much attention has been given to the problem of outlier detection, whose aim is to detect outliers – objects which behave in an unexpected way or have abnormal properties....

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different from the most of the individuals of t...

Multi-label learning is a challenge problem in computer vision fields. Since annotating a multilabel instance costs greatly, multi-label classification has become a hot topic research. State-of-theart active learning methods either annotate all the relevant samples without diagnosing discriminative information in the labels or annotate only limited discriminative samples manually, that has weak...

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...