Potholes_kaggle No Outliers

## Overview

Potholes_kaggle No Outliers is a dataset for object detection tasks - it contains Pothole NFUH annotations for 657 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

ishaansehgal99/kubernetes-reformatted-remove-outliers dataset hosted on Hugging Face and contributed by the HF Datasets community

Here we present a dataset, MNIST4OD, of large size (number of dimensions and number of instances) suitable for Outliers Detection task.The dataset is based on the famous MNIST dataset (http://yann.lecun.com/exdb/mnist/).We build MNIST4OD in the following way:To distinguish between outliers and inliers, we choose the images belonging to a digit as inliers (e.g. digit 1) and we sample with uniform probability on the remaining images as outliers such as their number is equal to 10% of that of inliers. We repeat this dataset generation process for all digits. For implementation simplicity we then flatten the images (28 X 28) into vectors.Each file MNIST_x.csv.gz contains the corresponding dataset where the inlier class is equal to x.The data contains one instance (vector) in each line where the last column represents the outlier label (yes/no) of the data point. The data contains also a column which indicates the original image class (0-9).See the following numbers for a complete list of the statistics of each datasets ( Name | Instances | Dimensions | Number of Outliers in % ):MNIST_0 | 7594 | 784 | 10MNIST_1 | 8665 | 784 | 10MNIST_2 | 7689 | 784 | 10MNIST_3 | 7856 | 784 | 10MNIST_4 | 7507 | 784 | 10MNIST_5 | 6945 | 784 | 10MNIST_6 | 7564 | 784 | 10MNIST_7 | 8023 | 784 | 10MNIST_8 | 7508 | 784 | 10MNIST_9 | 7654 | 784 | 10

Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and p-values on the remaining data as if this were the original data collected. This standard “detect-and-forget” approach has been shown to be problematic, and in this article we highlight the fact that it can lead to invalid inference and show how recently developed tools in selective inference can be used to properly account for outlier detection and removal. Our inferential procedures apply to a general class of outlier removal procedures that includes several of the most commonly used approaches. We conduct simulations to corroborate the theoretical results, and we apply our method to three real datasets to illustrate how our inferential results can differ from the traditional detect-and-forget strategy. A companion R package, outference, implements these new procedures with an interface that matches the functions commonly used for inference with lm in R. Supplementary materials for this article are available online.

Blockchain data query: Send Transfer Volume (no outliers)

Dataset

This dataset was created by darionapolitanoreal

Released under Apache 2.0

Contents

*JESP  =  Journal of Experimental Social Psychology, CD  = Cognitive Development, CP  =  Cognitive Psychology, JADP  =  Journal of Applied Developmental Psychology, JECP  =  Journal of Experimental Cognitive Psychology, and JPSP  =  Journal of Personality and Social Psychology.

Median values, interquartile range (IQR) and Number of outliers.

Unsupervised outlier detection constitutes a crucial phase within data analysis and remains an open area of research. A good outlier detection algorithm should be computationally efficient, robust to tuning parameter selection, and perform consistently well across diverse underlying data distributions. We introduce Boundary Peeling, an unsupervised outlier detection algorithm. Boundary Peeling uses the average signed distance from iteratively peeled, flexible boundaries generated by one-class support vector machines to flag outliers. The method is similar to convex hull peeling but well suited for high-dimensional data and has flexibility to adapt to different distributions. Boundary Peeling has robust hyperparameter settings and, for increased flexibility, can be cast as an ensemble method. In unimodal and multimodal synthetic data simulations Boundary Peeling outperforms all state of the art methods when no outliers are present while maintaining comparable or superior performance in the presence of outliers. Boundary Peeling performs competitively or better in terms of correct classification, AUC, and processing time using semantically meaningful benchmark datasets.

The zip files contains 12338 datasets for outlier detection investigated in the following papers:(1) Instance space analysis for unsupervised outlier detection Authors : Sevvandi Kandanaarachchi, Mario A. Munoz, Kate Smith-Miles (2) On normalization and algorithm selection for unsupervised outlier detection Authors : Sevvandi Kandanaarachchi, Mario A. Munoz, Rob J. Hyndman, Kate Smith-MilesSome of these datasets were originally discussed in the paper: On the evaluation of unsupervised outlier detection:measures, datasets and an empirical studyAuthors : G. O. Campos, A, Zimek, J. Sander, R. J.G.B. Campello, B. Micenkova, E. Schubert, I. Assent, M.E. Houle.

Gene expression data have been presented as non-normalized (2-Ct*109) in all but the last two rows; this allows for the back-calculation of the raw threshold cycle (Ct) values so that the typical range of expression of each gene can be more easily assessed by interested individuals. The sample number fraction following the island name represents the number of outliers over the total number of samples for which a Mahalanobis distance could be calculated (rather than the number of samples analyzed from that site). Values representing aberrant levels for a particular response variable (i.e., that contributed to the heat map score) have been highlighted in bold. When there was a statistically significant difference (student’s t-test, p<0.05) between the outlier and non-outlier averages for a parameter (instead using normalized gene expression data), the lower of the two values has been underlined. No outliers were detected amongst the colonies sampled from Tuvuca (n = 8 samples analyzed in full) and Cicia (n = 8 samples analyzed in full). Fulaga sample 54 was also determined to be an outlier after imputation of missing data (discussed in the main text), though it is not featured in this table. In the “Color” column, the values are as follows: 1 = normal, 2 = pale, 3 = very pale, and 4 = bleached. PAR = photosynthetically active radiation. SA = surface area. GCP = genome copy proportion. Ma Dis = Mahalanobis distance. “.” = missing data.

In the R programming language, there are many packages related to a topic. Outliers is one of them. Dataset and descriptions of packages related to outliers in R:

Package_Name: Package name Update_Date: The last update date of the package Version: Package version Depend: Package Depend License: Package License Needs Compilation: Need a compilation or not? URL: The package's website Encoding: UTF-8 or not Maintainer: Package maintainer Vignette_builder: Vignette builder Title: The title of the package Downloads1month: Number of downloads in the last 1 month Downloads6month: Number of downloads in the last 6 month Downloads12month: Number of downloads in the last 12 month

BackgroundThe causes of reduced aerobic exercise capacity (ExCap) in chronic kidney disease (CKD) are multifactorial, possibly involving the accumulation of tryptophan (TRP) metabolites such as kynurenine (KYN) and kynurenic acid (KYNA), known as kynurenines. Their relationship to ExCap has yet to be studied in CKD. We hypothesised that aerobic ExCap would be negatively associated with plasma levels of TRP, KYN and KYNA in CKD.MethodsWe included 102 patients with non-dialysis CKD stages 2–5 (CKD 2–3, n = 54; CKD 4–5, n = 48) and 54 healthy controls, age- and sex-matched with the CKD 2–3 group. ExCap was assessed as peak workload during a maximal cycle ergometer test. Plasma KYN, KYNA and TRP were determined by high-performance liquid chromatography. Kidney function was evaluated by glomerular filtration rate (GFR) and estimated GFR. The CKD 2–3 group and healthy controls repeated tests after five years. The association between TRP, KYN, KYNA and ExCap in CKD was assessed using a generalised linear model.ResultsAt baseline, there were significant differences between all groups in aerobic ExCap, KYN, KYNA, TRP and KYN/TRP. KYNA increased in CKD 2–3 during the follow-up period. In CKD 2–5, KYNA, KYN/TRP and KYNA/KYN were all significantly negatively associated with ExCap at baseline, whereas KYN and TRP were not. Kynurenines were significantly correlated with GFR (p < 0.001 for all). Including GFR in the statistical model, no kynurenines were independently associated with ExCap at baseline. At follow-up, the increase in KYN and KYN/TRP was related to a decrease in ExCap in CKD 2–3. After adjusting for GFR, increase in KYN/TRP remained an independent significant predictor of a decline in ExCap in CKD 2–3.ConclusionAerobic ExCap was inversely associated with plasma levels of kynurenines in CKD at baseline and follow-up.

The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address this problem and develop sequential and distributed algorithms that are significantly more efficient than state-of-the-art methods while still guaranteeing the same outliers. By combining simple but effective indexing and disk block accessing techniques, we have developed a sequential algorithm iOrca that is up to an order-of-magnitude faster than the state-of-the-art. The indexing scheme is based on sorting the data points in order of increasing distance from a fixed reference point and then accessing those points based on this sorted order. To speed up the basic outlier detection technique, we develop two distributed algorithms (DOoR and iDOoR) for modern distributed multi-core clusters of machines, connected on a ring topology. The first algorithm passes data blocks from each machine around the ring, incrementally updating the nearest neighbors of the points passed. By maintaining a cutoff threshold, it is able to prune a large number of points in a distributed fashion. The second distributed algorithm extends this basic idea with the indexing scheme discussed earlier. In our experiments, both distributed algorithms exhibit significant improvements compared to the state-of-the-art distributed methods.

Dataset

This dataset was created by maverick_23_45

Released under Apache 2.0

Contents

Number of features with 0 through 4 detected outliers in the control group of rat RNA-seq data.

Japan Composite Index: Coincident Series: Number Outlier Replacement data was reported at 105.200 2015=100 in Oct 2018. This records an increase from the previous number of 102.200 2015=100 for Sep 2018. Japan Composite Index: Coincident Series: Number Outlier Replacement data is updated monthly, averaging 94.450 2015=100 from Jan 1985 (Median) to Oct 2018, with 406 observations. The data reached an all-time high of 108.600 2015=100 in Oct 1990 and a record low of 63.700 2015=100 in Mar 2009. Japan Composite Index: Coincident Series: Number Outlier Replacement data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Japan – Table JP.S001: Leading Indicators: 2015=100.

The following report outlines the workflow used to optimize your Find Outliers result:Initial Data Assessment.There were 721 valid input features.GRM Properties:Min0.0000Max157.0200Mean9.1692Std. Dev.8.4220There were 4 outlier locations; these will not be used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band selected was based on peak clustering found at 1894.5039 Meters.Outlier AnalysisCreating the random reference distribution with 499 permutations.There are 248 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.There are 30 statistically significant high outlier features.There are 7 statistically significant low outlier features.There are 202 features part of statistically significant low clusters.There are 9 features part of statistically significant high clusters.OutputPink output features are part of a cluster of high GRM values.Light Blue output features are part of a cluster of low GRM values.Red output features represent high outliers within a cluster of low GRM values.Blue output features represent low outliers within a cluster of high GRM values.

Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule Abstract: Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

There has been a tremendous increase in the volume of sensor data collected over the last decade for different monitoring tasks. For example, petabytes of earth science data are collected from modern satellites, in-situ sensors and different climate models. Similarly, huge amount of flight operational data is downloaded for different commercial airlines. These different types of datasets need to be analyzed for finding outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets are physically stored at different geographical locations with only a subset of features available at any location. Moving these petabytes of data to a single location may waste a lot of bandwidth. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the entire data without moving all the data to a single location. The method we propose only centralizes a very small sample from the different data subsets at different locations. We analytically prove and experimentally verify that the algorithm offers high accuracy compared to complete centralization with only a fraction of the communication cost. We show that our algorithm is highly relevant to both earth sciences and aeronautics by describing applications in these domains. The performance of the algorithm is demonstrated on two large publicly available datasets: (1) the NASA MODIS satellite images and (2) a simulated aviation dataset generated by the ‘Commercial Modular Aero-Propulsion System Simulation’ (CMAPSS).