Questions tagged [outlier]

For questions regarding outliers or unusual points in the data.

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1answer
33 views

Is it a good idea to use parallel coordinates for visualising outliers? [closed]

I tried using parallel coordinates to visualize outliers. Is it fundamentally correct?
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0answers
24 views

Novelty prediction Using DBSCAN on “unseen data”

I am trying to build an unsupervised learning model, which will be able to predict outliers on "unseen data." The algorithm I chose is DBSCAN (Density-based spatial clustering of ...
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0answers
19 views

How is convex hull method used in outlier detection?

I think the slides are bit unclear on what they want to say. Can someone elaborate this with example.
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1answer
17 views

How should a stateless data transformation be applied in regard to train/test split?

I want to apply spatial sign transformation to my data, but unlike other transformations this one is stateless. I am using sklearn and normallly i would first use ...
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1answer
23 views

Distance between any two points after DBSCAN

DBSCAN is a clustering model which is robust to detect the outliers also. A parameter $\epsilon$ i.e. radius is an input of the algorithm, a point is said to be outlier if it's circle with radius $\...
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0answers
21 views

Data preprocessing - Time Series data resets its values - Detection & Correction

1. Summarize the problem I currently trying to work with time series data from sensors which has some problems regarding resetting it values. For example some cumulative values gets reset and don't ...
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1answer
20 views

Real-Time Outlier/Anomaly Detection?

My data is the usage/playing statistics for players of a specific game. One data point for a user is aggregated statistics for one week. The goal is to be able to detect when the account of the player ...
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1answer
28 views

What if outliers still exist after variable transformation?

I have a variable with a skewed distribution. I applied BoxCox transformation and now the variable follows a Gaussian distribution. But, as seen in the image below in the boxplot, outliers still ...
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0answers
40 views

Model Tree M5 - Robustness to Data Quality Issues

I am currently investigating the M5 tree algorithm by Quinlan(1992) link here: https://sci2s.ugr.es/keel/pdf/algorithm/congreso/1992-Quinlan-AI.pdf An example of a linear regression model of the ...
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0answers
12 views

using regression method for payroll monthly controls

I serve as internal auditor in few clients ,one of my client has thousands of employees in different location, most of them in the head office, the client looks for corporate control for the salary ...
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1answer
44 views

How to aggregate data inserted by users to avoid outliers?

I'm developing a new application based on machine learning. In this application users can insert new data to improve the prediction system. As you may guess, users could insert data that doesn't make ...
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1answer
24 views

Clustering method for 2-D data that self-detects number of clusters and takes care of outliers

Assuming I have data that looks something like that: I'm looking for a method or algorithm that can perform the clustering (e.g. as shown in the picture), that automatically determines the optimal ...
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0answers
70 views

How can interparet shap.summary_plot and its gray color concerning outliers/anomaly?

I inspired by this notebook, and I'm experimenting IsolationForest algorithm using scikit-learn==0.22.2.post1 for anomaly ...
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1answer
39 views

Problem with Median Absolute Deviation

I am using Median Absolute Deviation(MAD) for outlier detection. But the problem with MAD is that if 50% or more values in a sample are identical, then MAD = 0 which is not desirable. Is there any way ...
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0answers
92 views

Does Double Median Absolute Deviation work for every distribution for the purpose of outlier detection?

I am using Double Median Absolute Deviation for finding outliers in a 1-D data. As mean with standard deviation gets influenced easily by outliers, that's why I chose median based approach. And the ...
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1answer
44 views

Anomaly Detection

I have a problem where I want to identify Vendors with unusual high amount invoices. What would be the best way to identify such invoices? I am trying to use Isolation Forest but having trouble in ...
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0answers
12 views

Robust Gaussian Fit

I have tried to find some literature on robust gaussian fits, all I could find was good old EM gaussian mixtures. The question is : given a mixture of gaussians, find the dominant one around a given ...
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0answers
11 views

Finding threshold value for right-skewed data (bivariate)

Intuition of the Problem Suppose you have a dataset of two columns, X and Y, and I plot them using a bar plot. The bar plot shows that there are a lot of values in the first two bars on the left, and ...
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2answers
249 views

What to replace outliers with? (supermarket transaction data)

I have a transaction dataset from a supermarket. Let's say the average spend is $50. I want to get each customer's average spend and rank them based on where they fall based on this $50 average spend. ...
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1answer
27 views

The robustness of the Frobenius and L2,1 norms to the outlier

I have a question about the properties of the Frobenius and L$_{2,1}$ norms. Why is the L$_{2,1}$ norm more robust to the outlier than the Frobenius norm? PS: For a matrix $A\in\mathbb{R}^{n\times d}$,...
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0answers
10 views

Figuring out what's wrong with the box plot. Outliers?

What a 'box plot' of this kind has to say? that basically I have a lot of outliers and I should focus on data in proximity of zero?
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1answer
42 views

How to find anomalies in (almost) constant stream of data?

I have a process that (simply put), starts every 5 minutes, collects data, and put that data into the database. More detailed explanation would be that process starts, collects data (which takes some ...
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0answers
18 views

Converting the continuous numerical features into gaussian distribution and how to deal with NaN values after that?

I have a dataset in which there are few continuous numerical features that distribution over them is non gaussian and this means, skewness is nonzero (positive or negative). I read that it is good to ...
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0answers
52 views

Intuition behind One Class SVM (Scholkopf)

I am trying to understand the intuition behind the idea of finding a hyperplane that separates the training data from the origin in the feature space. Why separation from origin with a hyperplane ...
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0answers
32 views

Classification problems - Finding Target variable outliers in data

We have Z-score, IQR etc to identify outliers in data. This could be used to eliminate outliers even in labels. For e.g. if the target variable is a housing price, we could use inter-quartile ranges ...
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1answer
35 views

Determining extreme outliers from boxplot (by eye) [closed]

I have a normalised dataset with range 0,1. I have created boxplots for every feature in the dataset and need to identify which features have extreme outliers (by looking at their boxplots). However, ...
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1answer
64 views

What can help decrease outliers' influence on non-tree models?

I have a feature with all the values between 0 and 1 except few outliers larger than 1. I am trying to collect all the methods that can help to decrease outliers' influence on non-tree models: ...
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1answer
431 views

Can a novelty detection model overfit?

Can a novelty detection model overfit? In novelty detection, the model is trained on normal data instances (not polluted by outliers) where no labels are used in the training process, while validated ...
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1answer
45 views

Word representation that gives more weight to terms frequent in corpus?

The tf-idf discounts the words that appear in a lot of documents in the corpus. I am constructing an anomaly detection text classification algorithm that is trained only on valid documents. Later I ...
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1answer
192 views

Algorithms for Anomaly Detection of Event Sequence Data [Python/R]

I am building an anomaly detection system of event sequence data (transactions). For each timestep, a transaction can be in any of 76 different stages. My dataset is therefore a 3D array of size(m,t,N)...
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1answer
56 views

Mathematical way of identifying wrong suggestions or outliers

I have a hypothetical scenario where i have 100 classifiers to which if a person's name is given as input, it will return a class for the person. Eg. Input1 -...
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1answer
157 views

Feature Selection and Outlier Detection

How does feature selection impact outlier detection and also, removing outliers impact feature selection? It could be a basic question. However, just to know the boundaries, I asked. Thanks in advance....
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1answer
336 views

Appropriate objective function and evaluation metric when I DO care about outliers?

I am reading these two pages: xgboost documentation Post on evaluation metrics I have a dataset where I am trying to predict future spend at the user level. A lot of our spend comes from large ...
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2answers
43 views

Boosted tree regression loss function when data has occasionally very large values to predict?

I have a regression problem where most of my target variables are down in the range 5-30, but occasionally the target variable will spike up to 100, 500, or even 5000. These values are not spurious ...
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1answer
94 views

Semi-supervised anomaly detection

I am currently exploring anomaly detection methods for my work and, basically I have gone through Local Oulier Factor and Isolation Forests, both unsupervised methods. Now, the thing is, there might ...
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2answers
1k views

When should you remove outliers?

Let's say I've found some outliers in a column in my dataset and have decided to remove them. Should I do this before or after I split the dataset into train/test sets?
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1answer
133 views

Removing outliers with orders of magnitude differences

I have a dataset of virtual currency earn and spend events from a mobile game app. Unfortunately, people cheat in the game to get more currencies. These cheaters use different techniques so its quite ...
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0answers
64 views

Is it necessary to transform data to normal distribution when removing outliers for xgboost?

sorry if this is statistics 101 but i cannot find a similar question. I am wanting to use xgboost to classify my data in two classifications. my data is numerical (financial statement data) and i can ...
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1answer
46 views

How to increase number of outliers in a dataset?

I have a dataset with 1000 rows and 4 columns with 3 outliers .I want to add another 7 outliers related to them for detection by clustering. ...
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2answers
5k views

Remove Outliers from Dataframe using pandas in Python

I would like to remove outliers from my dataset. It looks like this: ...
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0answers
22 views

Types of artificial anomalies

I am working on some algorithms for anomaly detection The dataset is clean our anomalies so I want to add some artificial anomalies. I have added some anomalies. I get the maximum value of the ...
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1answer
165 views

Pre-processing - Removing outliers

I have two files, a training data with a label field and a test data without the label field. I have plotted a field "A" in train data: It looks like outliers are 4,5,6 and should be removed. Now ...
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1answer
60 views

Outlier/Anomaly Detection History

I have been reading about different methods of anomaly detection, their structure and the way they work. Recently I have been trying to find some scholar articles, writings or books where I can learn ...
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2answers
105 views

Right order for Data preparation in Machine Learning

For the below mentioned steps of data preparation Outlier detection/treatment Data imputation Data scaling/standardisation Class balancing There are two sub questions Should each of these steps ...
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0answers
33 views

Can someone provide me the code of the MiLoF(Memory Efficient Local Outlier Factor) algorithm?

I have to code the MiLoF algorithm for detecting outliers in an unsupervised manner using non-stationary data. I am attaching the paper which explains the algorithm here. However, there are many ...
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0answers
42 views

Custom Decision Function for Custom Outlier Detection Algorithm

I have built a custom algorithm for semi-supervised anomaly detection and here is my output example as following with probability threshold set to 0.05 and 1 = outlier, 0 = inlier: ...
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2answers
44 views

What is the most effective unsupervised ML algorithm to use when outliers are present in data set?

I am analyzing a portfolio of about 225 stocks and have gotten data for each of them based on their "Price/Earnings ratio", "Return on Assets", and "Earnings per share growth". I would like to cluster ...
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0answers
30 views

is it beneficial to use high-order n-grams as feature vectors for web anomaly detection?

i am studying about the use of n-gram models to classify web attacks based on several parameters like, requested resources, query parameters and attributes, characters distribution and so on. Most ...
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2answers
2k views

How to tackle too many outliers in dataset

I boxplot all of my columns with seaborn boxplot in order to know how many outliers that i have, surprisingly there're too many outliers and so i can remove the outliers because i'm afraid with too ...
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2answers
77 views

Many separation line using RBF kernel in SVM

Below is my code, it take a range of a number, creates a new column label that contains either -1 or 1. In case the number is ...