Outlier detection is an important and attractive problem in knowledge discovery in large datasets. Instead of detecting an object as an outlier, we study detecting the n most outstanding outliers, i.e. the top-n outlier detection. Further, we consider the problem of combining the top-n outlier lists from various individual detection methods. A general framework of ensemble learning in the top-n...

The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to res...

Many biological high-throughput datasets, such as targeted amplicon-based and metagenomic sequencing data, are compositional. A common exploratory data analysis task is to infer robust statistical associations between high-dimensional microbial compositions habitat- or host-related covariates. To address this, a general regression framework RobRegCC (Robust Regression with Compositional Covaria...

In this paper we will propose a new probabilistic topic model to score the expertise of participants on the projects that they contribute to based on their previous experience. Based on each participant’s score, we rank participants and define those who have the lowest scores as outlier participants. Since the focus of our study is on outliers, we name the model as Mining Outlier Participants f...

background: in oil refinery plants, shift working is inevitable . shift work is associated with depression; therefore this prospective study was carried out in tehran shahid tondguyan oil refinery in order to determine the prevalence of depression in shift workers and to assess the effects of associated factors with depression among shift workers. in the present study, the shifting work system ...

Two new techniques based on nonparametric estimation of probability densities are introduced which improve on the performance of equivalent robust methods currently employed in computer vision. The first technique draws from the projection pursuit paradigm in statistics, and carries out regression Mestimation with a weak dependence on the accuracy of the scale estimate. The second technique exp...

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...

In this paper, we experimentally evaluate outlier detection methods, which detect data points that are far away from others in a data set, in terms of improving the prediction performance of fault-prone module detection models. In the experiment, we compared two outlier detection methods (MOA, LOFM) each applied to three wellknown fault-prone module detection models (LDA, LRA, CT). The result s...

In chemical industries, process operations are usually comprised of several discrete operating regions with distributions that drift over time. These complexities complicate outlier detection in the presence of intrinsic process dynamics. In this article, we consider the problem of detecting univariate outliers in dynamic systems with multiple operating points. A novel method combining the time...