Questions tagged [outlier]

For questions regarding outliers or unusual points in the data.

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65 views

What is meant by outliers in text data set. How to detect them?

I know that outliers are present in data but their behaviour varies a lot from remaining data points. But today while learning about naive-Bayes they mentioned that naive-Bayes can affected by the ...
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0answers
13 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
51 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
242 views

which outlier detection technique?

I'm new to data science. I have a question on anomaly detection techniques. There are several anomaly detection techniques such as statistical, density based, depth based, clustering, etc.. Given a ...
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1answer
25 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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3answers
6k views

Difference between Global Outlier and Contextual Outlier?

I am studying "Data Mining: Concepts and Techniques" by Han, Kamber & Pei. In Chapter 12 "Outlier Detection", they have stated that there are 3 types of outliers: Global Outlier - deviates ...
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1answer
45 views

How to distinguish between normal fluctuation and outliers in ARIMA model?

I have a dataset about sales per day of certain products at the ITEM/DAY/STORE level , I've plotted the series and visually examined it for any outliers, volatility, or irregularities. And this is ...
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0answers
18 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: ...
5
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2answers
296 views

How to scale outputs from AutoEncoder from multiple models?

I have a problem for which I have not been able to find any answers in my search so far. BACKGROUND I am working on an anomaly detection problem on machines utilising an auto-encoder. I am building ...
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3answers
1k views

How to use Autoencoders for outlier detection on images

I have a bunch of images taken from a camera showing a pipe and would like to detect if the pipe is leaking or not. There are very few examples of leaking pipe in the data set. So considering this ...
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2answers
63 views

How to improve identification of outliers for removal

I have many datasets where the measured value is either "normal" (i.e. the process is running" or abnormal (i.e. process is not running). Unfortunately, I don't have a measurement that clearly ...
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1answer
33 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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1answer
381 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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2answers
34 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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1answer
25 views

Are cluster feature and micro-cluster good summury statics for outlier detection in high dimensional data streams?

I'm dealing with outlier detection in data streams. I'm looking for a way to summarize my data and obtain important statistics such as means and variance, etc. I want to know if the cluster features ...
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1answer
33 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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2answers
39 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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1answer
303 views

Isolation forest - grouped by

I'm trying to use isolation forest algorithm for outliers detection. Data has 2 columns: id and REV. Below code gives me ...
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1answer
60 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....
2
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1answer
46 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
86 views

Anomaly detection in nominal big data

I have to apply an anomaly detection algorithm on big data, the values of each column on my dataframe are nominal and vary over 10000 times, the algorithms I've found only accept numeric values, is ...
2
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1answer
21 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
180 views
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2answers
69 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 ...
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2answers
231 views

Replace Values in Vector on Specific Place in R

I want to make $5^{th}$,$10^{th}$,$15^{th}$,$20^{th}$ and $25^{th}$ values of vector an outlier in all xs by using x1 [5]+OT1,...
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2answers
2k 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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1answer
98 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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3answers
12k views

How to remove outliers using box-plot?

I have data of a metric grouped date wise. I have plotted the data, now, how do I remove the values outside the range of the boxplot (outliers)? All the ['AVG'] data is in a single column, I need it ...
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0answers
27 views

DBSCAN vs RANSAC for outlier detection

As simple as the title: which one is best for outlier detection between DBSCAN and RANSAC? What are pros and cons of each model?
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3answers
104 views

Is my model over fitting or not?

I have 50000 observations with 70% positive and 30% negative target variable. I'm getting accuracy of around 96-99% which seems unreal of course and I'm worried that my model is over-fitting which I ...
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2answers
29 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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2answers
287 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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2answers
304 views

Explainable anomaly detection

There are plenty of working for explaining prediction in supervised learning (e.g. SHAP values, LIME). What about for anomaly detection in unsupervised learning? Is there any model for which there ...
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1answer
60 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
20 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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0answers
12 views

Is there a way we can narrow down the best range describing a dataset (other than IQR and Min-max)?

I have a dataset of the prices of products for different categories. I want to get the best range which can describe the prices of that category using the Product Data. I have already applied IQR and ...
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2answers
72 views

How to make multiple regression perform better for outliers? (without reducing effect of them)

I have a small dataset(about 60 samples) and I need it to predict well for high target values. There are only a few high values and all models I tried perform poorly for these high values. So I ...
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0answers
19 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
51 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
51 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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0answers
19 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
25 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
458 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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0answers
23 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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1answer
99 views

Isolation Forest: simple example

Can some one please explain Isolation Forests more clearly? Everywhere I search, I find the same explanation: Isolation Forest ‘isolates’ observations by randomly selecting a feature and then ...
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3answers
183 views

Standard deviation as outlier detection

I have a quite basic question: A standard deviation is defined such that around ~66 % of the data lies within it. And around ~99 % within three standard deviations. When I wanna' use the standard ...
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0answers
21 views

Which outlier detection algorithms give a breakdown of the contribution from each feature?

I am looking for an algorithm that outputs a breakdown of which features contributed the most towards a data point being labelled as an outlier. It can be supervised or unsupervised. At the moment, ...
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2answers
4k views

When to remove outlier in preparing features for machine learning algorithm

I have a numeric variable (price) and it has a long tail in both training and test data sets. I found that if you remove the highest 1% of the value in both train and test data set for this variable, ...
2
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2answers
257 views

General methods outlier detection

What are general methods for outlier detection that do not assume any underlying distribution in the data? I have a dataset with the prizes of the rents in London, as well as their location, number of ...
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1answer
38 views

Clustering unbalanced dataset

The data I am working on has some really large price values and some really small values. What I did was first perform feature bagging on the data and got them labelled to (0,1) and then did ...