“Target System” ⇒ “Atheros AR7xxx/AR9xxx” My V3 Board specifics brief are as follows: The board is very similar to V2 board but V3 is with new processor Qualcomm Atheros QCA9531. I recently build TL-MR 3420 V3 firmware which was not yet supported by openwrt. Baggingīagging (bootstrap aggregating) is an ensemble method to reduce the variance of the results by inducing diversity. The following subsection introduces a method to create diversity in an outlier detection ensemble. Therefore, the correlation should be low between the results. It is not meaningful to combine multiple instances of identical results, because no new information is gained. Second, the detectors have to provide results that are not identical. If the detectors assign outliers randomly, then the outlier detection ensemble also provides random results. First, the detectors have to provide results that are more accurate than random classification. The detectors have to be accurate and provide diverse results to benefit from their combination. The selection of KNN and LOF is motivated by the results in, which show that KNN and LOF can be combined successfully. These outlier detection algorithms are well established and commonly utilized in outlier detection ensembles. We combine the following two outlier detection algorithms: k-nearest neighbor (KNN) and local outlier factor (LOF). Therefore, our approach uses a smaller number of outlier examples than the existing work.
4 use from a single example to \(10\%\) outlier examples. The utilization of a limited number of outlier examples has been studied in ( \(10\%\) of the available outliers). Instead, they propose new algorithms, which utilize the individual outlier examples. The works in do not modify the parameters of the detectors. Unlike the previous work in, our proposed approach optimizes the parameters of the detectors using outlier examples. Every feature has an equal probability of being selected. This approach is called feature bagging see for more details. The existing work in samples a random subset of the available features for outlier detection. A feature is a single dimension of the analyzed data see the beginning of Sect. To further improve the efficiency of the outlier detection, we sample a subset of the available features in the analyzed data. If the combination of detectors does not detect outliers in the given data, even with suitable parameter values, then the optimization will not benefit the outlier detection ensemble. Additionally, the detectors are required to detect outliers in the given data. 2.1 and for more information on the outlier scores. The optimization is suitable for a combination of detectors, which (1) provide scores as the magnitude of an observation being an outlier and (2) utilize adjustable parameters.
Section 6 discusses about the acquired results and concludes this article. Section 2 defines the outlier detection algorithms and outlier detection ensembles in detail. The optimization method is introduced in detail in Sect. The optimization improves the efficiency of the outlier detection, which is empirically validated in Sect. In the context of our work, a limited number of outlier examples range from a single example to \(10\%\) of the available outliers for experiments. An outlier detection ensemble is a combination of detectors see Sect. The outlier detection algorithms are called detectors. We propose an approach for optimizing outlier detection ensembles by automatically adjusting the parameters of the combined outlier detection algorithms using a limited number of outlier examples. Outlier detection has been successfully applied in different fields. Other names for outliers include fault, intrusion and anomaly. It consists of unusual, unexpected and new information in comparison with inliers. An outlier can entail interesting information. Normal and expected data observations are called inliers. Outliers deviate significantly from the expectations. An outlier is an unexpected data observation that does not match the existing data or assumptions of how the observations are generated. Outlier detection is an important form of data analysis.