Questions tagged [outlier]

For questions regarding outliers or unusual points in the data.

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2answers
121 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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2answers
306 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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1answer
225 views

Probabilistic Outlier Detection (edited + clarified)

Measured data $D \in \mathbb{R}^3$, every $d^i \in D$ is $d^i_{(x)}$, where the $x=[x_1, x_2]$. Simply said, the measured data are function of $x$. It is known, the dependency is linear, such as: $$...
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0answers
137 views

Can we use doc2vec to detect outlier documents?

I have a set of documents and I want to identify and remove the outlier documents. I am just wondering if doc2vec can be used for this task. Or are there any recently evolved, promising algorithms ...
1
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1answer
27 views

Should we identify outliers from population prior to taking sample?

I am revising undergrad statistics course via this course, where i am learning techniques to pull out sample from population. While ensuring that sample is a decent representative of the population, ...
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0answers
42 views

Taking advatage of the frequency of an outlier

I'm doing outlier detection (Conditional Outliers) on a multivariate time series. The outliers appear every 2 weeks $\pm$ 4 days. How can I incorporate this prior in my models, to reduce the number ...
2
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3answers
663 views

Why replacing null values with outliers?

I have been watching a tutorial on stock price prediction with multivariate linear regression and the tutor replaces missing value data, NaN, with the outlier -99999. Why and how do replacements like ...
1
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1answer
16k views

Remove Local Outliers from Dataframe using pandas

Could someone please suggest how to remove local outliers from the dataframe? I have the code to detect the local outliers, but I need help removing them(setting these values to zero) in the dataframe....
1
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1answer
757 views

LOF gives same number of outliers irrespective of parameters

I am running lof algorithm for around 100k 2d points. Each time, I run the lof algorithm with different n_neighbours parameter, I get the same number of points as outliers. It's always 10% of the ...
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2answers
68 views

Unsupervised Clustering of 6-d vectors

I have 17k vectors, each having 6 points, I want to cluster the vectors based on nature of points, eg. linearly increasing into one cluster, convex in another cluster, concave in another, decreasing ...
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1answer
4k views

Implement Sliding Window Algorithm to Detect Spikes

How do I implement sliding window algorithm with a window size of 10 and visualize the data iteratively to see spikes/possible outliers in the dataframe, using python? Please help a beginner.
3
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2answers
1k views

Outlier detection with sklearn

I've been reading the sklearn documentation on outlier detection, and even the examples provided by the documentation. Once I have fitted my dataset, all I can do is apply the predict function to the ...
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2answers
1k views

Using local outlier factor score to detect outliers at run time

I am using LOF ( local Outlier factor) to detect outliers in my data. I get LOF score as outlier distance. this unsupervised learning doesnt help to detect outliers at run time. So I want to use my ...
4
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3answers
6k views

Handling outliers and Null values in Decision tree

Outliers : As I understand, decision trees are robust to outliers. Can anybody please confirm if my hypothesis is right with an example? (What if I have a features ranging from 0 to 9 but there is an ...
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1answer
145 views

Understanding a Box plot

I have a dataset and I have tried plotting it in a box plot after loading the csv in usings pandas. The plot itself is given below. I need to understand why the x1 ...
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1answer
125 views

Principal Component Analysis and abnormal data

I know that PCA is good in differentiating between anomalies and normal data and it helps to differentiate between them when it tries to transfer the data to another dimension. I mean it can somehow ...
4
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1answer
5k views

Multivariate outlier detection with isolation forest..How to detect most effective features?

I am trying to detect outliers in my data-set with 5000 observations and 800 features. I have followed the simple steps told in http://scikit-learn.org/stable/auto_examples/ensemble/...
2
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2answers
287 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
37 views

outlier detection in time serie without using windows [closed]

I would like to know if it's possible to detect outliers in a time-serie with an outlier score computed given the whole dataset and not given windows
1
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0answers
353 views

Isolation forest results every value -1

I am trying out isolation forest to detect outliers in a specific target column of my dataset. The dataset contains 188 rows of data with 178 rows with the same value for that target column and the ...
4
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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, ...
1
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1answer
259 views

An outlier detection for this data

I have a vector and want to detect outliers in it. I need an outlier detection method (a non-parametric method) which can detect red points as outliers. Edit: I have a lot of vectors like this. The ...
5
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3answers
2k views

To detect unauthorized access using outlier detection

I am working on project where my task is to find unauthorized access using any machine learning technique. Let me clear my problem definition. UserA access website using chrome browser from windows ...
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1answer
55 views

Differentiating between normal and attack clusters

In the below figure, we plotted some data of sensors in normal condition and under attack(outlier): 1. Green points are normal samples in the training dataset. 2. Cyan and red points are normal ...
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1answer
754 views

Isolation Forest height limit absent in SkLearn implementation

In the original publication of the Isolation Forest algorithm, the authors mention a height limit parameter to control the granularity of the algorithm. I did not find that explicit parameter on the ...
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4answers
461 views

How to detect anomalous points

As is clear from the figure, the blue points, which don't follow the trend, are anomalous points. I'm wondering about the best non-parametric method to detect those points. I have tested some ...
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0answers
366 views

Sensitivity analysis in outlier explanation

I am trying to find the outlier explanation using the sensitivity analysis. Let’s consider that my dataset contains 19 different input values and 1 output value (So overall 20 different columns are ...
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3answers
6k views

What is the difference between outlier detection and anomaly detection?

I would like to know the difference in terms of applications (e.g. which one is credit card fraud detection?) and in terms of used techniques. Example papers which define the task would be welcome.
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2answers
2k views

How to decide for the contamination value (proportion of the outliers) in my dataset?

I should decide on the contamination value while using the Isolation Forests algorithm (I am using the sklearn implementation). Otherwise, sklearn's default is 0.1. I am worried if I decide for this ...
2
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0answers
86 views

Outlier detection: Should the metric used in kNN take into account variance explained by each coordinate?

After applying PCA and working with the reduced dataset, I want to delete the outliers. To do this my idea is to compute the kNN-graph and delete those vertices (points) that have an inner degree of 0....
3
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2answers
891 views

How to increase the weight when it comes to outlier detection

Let's say we have feature A, B, C, D, E to represent one observation in an outlier detection model. We are using scikit-learn outlier detection in our case. AFAIK, if we normalize all the features, ...
6
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2answers
913 views

Anomaly detection in time series

The use case : Everyday, we have metrics that are established daily to check that our systems are doing fine. From time to times, bugs occur in the workflow building these metrics, and I have to ...
1
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1answer
2k views

Can the Generative Adversarial Network useful for Outlier detection and Outlier explanation in a high dimentional numerical data?

I have been building a model to find explanation of Outliers in a high dimensional numerical data, generated from many sensors. The data contains more than 350 different fields and each field has ...
2
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1answer
1k views

Outlier detection on categorical network log data

I am working with a completely categorical network log data that consists of source ip address, destination ip address, source port, destination port, protocol. Data Preprocessing performed : ...
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2answers
127 views

Notion of cluster centers and cluster comparison in Density Based Algorithms

I have done some research on clustering algorithms since for my goal is to cluster noisy data and identify outliers or small clusters as anomalies. I consider my data noisy because of my main ...
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1answer
2k views

HDBSCAN Outlier Detection and labeling

I am using the HDBSCAN algorithm so as to perform unsupervised clustering and detect outliers. Based on the documentation there are two outputs from the clustering process that can give insight on ...
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2answers
2k views

Classification affected by a lot of outliers in features? How do you deal with outliers?

I am working on a classification problem and I found my data having a lot of outliers which has resulted in reduction in my recognition rate. I have tried rescaling, normalization techniques like ...
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1answer
132 views

Considering outliers in demand predictions

I have times series data with demand observations during months. I was wondering if, when computing demand predictions, I need to consider the outliers of the observations or not. What is your opinion ...
3
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3answers
7k 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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3answers
595 views

Detecting outlier with combining two vectors

I want to combine the following vectors in a way that just the red point (number 7) becomes inconsistent with other points( become an outlier and become distant from other points) and other points ...
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3answers
1k views

Which outlier detection can detect these outliers?

I have a vector and want to detect outliers in it. The following figure shows the distribution of the vector. Red points are outliers. Blue points are normal points. Yellow points are also normal. ...
3
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2answers
4k views

Outlier detection by unsupervised algorithm: Fraud Detection

I have set of 300,000 set of rows with credit card transactions and my job is to find outliers (suspicious transactions) in those dataset. I have created around 5 features (All continuous data, with ...
3
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2answers
802 views

Which Outlier Detection Method? Why?

For detecting an outlier in a vector I have tested different well known outlier detection methods. Finally, I used combination of different methods and an agreement between those methods. Now, a ...
0
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1answer
54 views

Outliers Approach

Having a schema which the majority of the values are IDs. Like this example (this isn't my real data): ...
0
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1answer
84 views

How can we detect the existence of outliers using mean and median?

How can we detect the existence of outliers using mean and median? Is it really possible to detect the existence of outliers in a set of data from their feature-wise mean and median? Suppose, I have ...
2
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2answers
765 views

Outlier detection and removal using scatter-plots and histograms

Suppose, we use the following code to generate scatter plots, ...
2
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1answer
393 views

Remove Outliers - Market Basket Analysis

I'm having some thoughts on whether I should remove the outliers. I'm trying to find the tags that are commonly used together. Imagine that I have the following dataset. The first column is the Tag_ID ...
2
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2answers
3k views

Dealing with outliers and z-scores

I am new to Data Science and have a few trivial questions, which I think are essential for my understanding of the basic data science techniques. I am building a function to calculate a social ...
2
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1answer
101 views

Some algorithms and approach for identification of specific patterns?

I am working on a hobby project where I have a data related to financial trades(such as stock trades). Now I want to analyse this data and extract possible ...
4
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2answers
81 views

Represent outlier days

I have hourly power consumption data for 30 days. On representing, each day data using a separate line, I get a plot as I want to highlight the days with abnormally high consumption (in other words, ...