Questions tagged [oversampling]

Sampling cases with differential probability, so that classes that occur rarely in the population occur more often in the training data. Does *not* address the problems in unbalanced classes.

Filter by
Sorted by
Tagged with
20
votes
1answer
17k views

Opinions about Oversampling in general, and the SMOTE algorithm in particular [closed]

What is your opinion about oversampling in classification in general, and the SMOTE algorithm in particular? Why would we not just apply a cost/penalty to adjust for imbalance in class data and any ...
0
votes
2answers
309 views

SMOTE in unbalanced dataset with binary features

after reading different posts about unbalanced datasets I didn't make my mind clear about my specific problem so that's why I'm posting a new question. In my case, I have a dataset with around 20K ...
0
votes
1answer
151 views

Adjusting precision recall curve for oversampling

I built a model for a binary target using oversampled data. The population target prevalence is 0.25. I oversampled to 0.5 by keeping the entirety of the minority class and sampling a portion of the ...
0
votes
0answers
14 views

How to tune an weighted voting ensemble method?

I am working on kidney cancer patients' data with 5 unbalanced labels. These codes are contained of Normalization, Oversampling on Feature Engineering part. A list of 9 ordinary Machine Learning ...
1
vote
1answer
723 views

Oversampling a multi-labeled data set

Given a data set where each individual data point can be assigned to more than 1 class (a multi-class, multi-label data set), are there any guidelines for calculating oversampling weights, i.e., the ...
0
votes
0answers
22 views

Does oversampling or undersampling not impact the coefficients of independent variables?

I have come across a few discussions on this site which state that random oversampling or undersampling doesn't impact the coefficients of the independent variables in logistic regression. Since the ...
0
votes
1answer
32 views

Training with oversampling

I'm building a Random Forest model over an unbalaced 4 class dataset. So far I understood how to use oversampling and train my model. My doubt was about when to perform Oversampling. I've already seen ...
2
votes
2answers
233 views

(When) Does simulating a larger sample from a small sample yield better results?

I am trying to classify a set of $p$ predictors into 5 classes. But my sample size $n$ is rather small, so I fear I won't get a very robust estimate. Now an idea would be to subset my data for each ...
0
votes
0answers
65 views

How H2O perform class balancing?

I wanto to perform class balancing using h2o autoML. I know there is a parameter class_sampling_factors that allow to specify the under/over sampling factor for ...
0
votes
0answers
22 views

Limits of oversampling

I have a dataset with an event rate of less than 0.3 percent. To improve the modeling results, I did some oversampling using SMOTE. I initially oversampled so that the event rate increases 10 times to ...
0
votes
0answers
14 views

How to oversample multivariate time series (sensor failure data)?

Let's say that I have a multivariate time series dataframe of sensors. Each sensor has its serial number, group, several statistics and column failure. Data about sensor's statistics are collected ...
0
votes
0answers
14 views

Denoising autoencoder with oversampling?

Denoising autoencoder is using noised added training samples to predict (original) training samples themselves. The goal is to denoise when being applied to the real sample. Here is an example of ...
1
vote
0answers
48 views

Will oversampling help with generalization (small imbalanced dataset)?

I have an imbalanced dataset (2:1 ratio) with about 60 patients and 80 features. I performed RFE + stratified cross validation to reduce the features to 15 and I get an AUC of 0.9 with Logistic ...
0
votes
0answers
49 views

Classification on rare events (~%3) and only categorical variables

I need to build a model based on about 10 independent variables, all categorical (only two of which are potentially ordinal), to predict a dichotomous output ('1': 3%; '0': 97%). To overcome the ...
1
vote
3answers
8k views

Running XGBoost with *highly* imbalanced data returns near 0% true positive rate. Tried SMOTE and it did not improve much. What else can I do?

I'm using XGBoost on a dataset of ~2.8M records of hard drive failures, where less than 200 are tagged as failures. After cleaning, there are 11 features in this dataset. Below is my ...
1
vote
0answers
38 views

When is oversampling preferable to undersampling and vice versa?

When data is unbalanced, that is, when the distribution of classes being predicted is very uneven (e.g. 90%/10% for two classes or 10%/15%/75% for three classes), many machine learning models have ...
2
votes
1answer
510 views

Oversampling methods for numerical data (regression)

There are many oversampling methods for categorical labels (for example SMOTE and Rose, etc.). But, are there oversampling method for numerical labels (the thing that I want to predict with my ...
1
vote
3answers
2k views

Does oversampling/undersampling change the distribution of the data?

I have an imbalanced dataset (10000 positives and 300 negatives) and have divided this into train and test sets. I perform oversampling/undersampling only on the train set since doing this on the test ...
0
votes
0answers
75 views

Is doing oversampling on train set and undersampling on test set correct?

I have an imbalanced dataset (95% in class 0 and 5% in class 1) and I am using machine learning for classification. The AUC(Area under ROC curve) was high (about 0.86) but AUPRC(Area under precision-...
1
vote
1answer
42 views

How do i re-train a final model after using oversampling?

I am a bit puzzled about the process of experimenting with a model and oversampling and then translating it to the final version of the model that will be used: I oversample the data (only the ...
1
vote
0answers
72 views

SMOTE and Lagged Observations

I'm doing a project about the effect of synthetic oversampling in a machine learning context (more precise SMOTE for the oversampling of the minority class of a highly imbalanced target variable). The ...
1
vote
3answers
870 views

Binary Classification in Imbalanced Data; Oversampling and Imputation

Together with two friends I participate in a university course about data mining in R and we chose the topic of bankruptcy prediction. We started with some "clean" data found on an "In class" kaggle ...
1
vote
0answers
118 views

influence of oversampling on Semi-supervised multi-label learning

I have suggested a semi-supervised approach for the hierarchical multi-label classification task. I have included the MLSMOTE oversampling technique as a pre-processing step, and then evaluate the ...
3
votes
0answers
237 views

Run time of SMOTE function in package DMwR

I have a dataframe with 930 000 rows and 220 variables. The objective is a binary classification but my response classes are imbalanced. (88% - 12%) I want to use SMOTE to artificially create ...
2
votes
1answer
51 views

Running SMOTE on unbalanced data

I've read a few answers to similar questions that advise the completion of SMOTE after splitting the data set into Train and Test sets however, the documentation and other examples I've seen run SMOTE ...
1
vote
1answer
506 views

Effects of Oversampling on Linear Regression

We often see that oversampling is a useful technique to combat skewed classes. But there is not much discussion about linear regression. If we just duplicate the original data, and train the linear ...
0
votes
1answer
377 views

Oversampling problems in prediction

I have a dataset that contains 284315 samples of class 0 and 492 of class 1. I know, that's huge. I heard about oversampling methods, so I did the following using the RandomOverSampler library: ...
2
votes
0answers
560 views

Logistic Regression Class Imbalance and the use of weighting and undersampling

I have been working on a machine learning model using Spark (binomial) LogisticRegression. The dataset has what I think is a high degree of imbalance - roughly 1% of rows are labelled as events. The ...
5
votes
1answer
5k views

Problem with classifier after using SMOTE to balance the data

We've ran into a problem while training a classifier on an unbalanced data set. The response is binary with 0 indicating 'non defaulter' and 1 indicating 'defaulter' (it's a credit scoring task). ...
12
votes
1answer
16k views

ROSE and SMOTE oversampling methods

Can somebody give me a brief explanation of the differences between those two resampling methods : ROSE and SMOTE ?
2
votes
1answer
647 views

ROC and false positive rate with over sampling

I'm modelling a rare event (say 1 in 10000) and I'm using an over sampled train set to cross validate and train my model. I'm using ROC as a global performance metric but there are business reasons ...
1
vote
0answers
10 views

Oversampling to Create Correlated Feature from Existing Feature

For demo purposes in Python, I would like to create an array or pandas df column that shares a high degree of correlation with an existing array. This is not as easy as it sounds and I don't think it ...
0
votes
0answers
72 views

Imbalanced class SVM prediction results using different validation data

I am trying to fit my data to a classifier using SVM. My data has 2 classes, the positive class which occurs with a probability of 0.002 and the negative class which is the dominant one. Suppose that ...
3
votes
0answers
4k views

Should SMOTE oversampling be done before or after holdout validation's training/testing split? [duplicate]

Originally, without SMOTE, my ML learning steps go like this: Feature vectorization split data into X_train, X_test, y_train, and y_test use X_train and y_train for machine learning predict/test ...
1
vote
0answers
121 views

While dealing with imbalanced classes, to what extent can we upsample a minority class? [duplicate]

I have my training data with the following approximate distribution: Negative events : 90,000 positive events : 5,000 Training a model would require to oversample the minority class (and might also ...
1
vote
0answers
380 views

Oversampling using SMOTE leading to bad predictions on test set

I have a dataset with an imbalanced binary target. One class accounts for about 94 % of the target variable. I used SMOTE to oversample the minority class but after the oversampling step when I train ...
7
votes
1answer
4k views

Normalization/standardization: Should one do this before oversampling/undersampling the data or after?

When working with imbalanced datasets, should one do one-hot encoding and data standardization before or after sampling techniques (such as oversampling or undersampling)?
4
votes
1answer
59 views

Does it makes sense to perturb images on-the-run while training CNNs?

I'm training a convolutional neural network by adding small perturbations (like rotation and shifting) to the images each time it gets a batch data. I think a better way of doing this could be to ...
2
votes
1answer
1k views

How to report the results of a cross-validation to a paper: Can I manually select the best results?

Good morning. With regard to the interpretation of the results of the N-fold cross-validation with over-sampling (SMOTE), I raise the question to write up the paper. Currently, I have performed 5-...
1
vote
1answer
319 views

Random Over Sampling to handle Data Imbalance

Was reading an article on imbalanced datasets where the event occurs and look at balancing the dataset. In that article, the event records were 2% of the total records. The author of the blog ...
35
votes
0answers
865 views

Are unbalanced datasets problematic, and (how) does oversampling (purport to) help? [duplicate]

TL;DR See title. Motivation I am hoping for a canonical answer along the lines of "(1) No, (2) Not applicable, because (1)", which we can use to close many wrong questions about unbalanced datasets ...
1
vote
0answers
102 views

Bias-Variance Tradeoff when using Oversampling Technique

Oversampling techniques (e.g. SMOTE) are often used when target values are not approximately equally represented. How does this technique affect bias and variance of the predictive model that is ...
9
votes
1answer
2k views

Oversampling: whole set or training set

I have a rather small dataset of 4 000 points (140 features) to feed to a NN binary classifier. The problem is only ~700 of them represent the second class. Is it more common to resample the whole ...
1
vote
0answers
573 views

random forest imbalanced data-over, under, Smote Sampling

I am using random forest model for an imbalanced dataset. The dependent variable is Yes=73, No=7100. I have 65 independent variables both factor and numeric. I have tried to develop models for ...
22
votes
2answers
17k views

Testing Classification on Oversampled Imbalance Data

I am working on severely imbalanced data. In literature, several methods are used to re-balance the data using re-sampling (over- or under-sampling). Two good approaches are: SMOTE: Synthetic ...
3
votes
2answers
86 views

Using patient characteristics to predict disease and confront with per hospital real cases

We have a large, nationwide prevalence study about the prevalence of healthcare-associated infections (HAI). We need to see if hospitals have more or less HAI than expected by their patients' ...
0
votes
1answer
286 views

Modeling Attrition: Imbalanced Training

First of all, let me say I am new to Machine Learning and am eager for any sort of feedback. I am attempting to create a predictive attrition model, and my training and test data each have ~ 17% ...
0
votes
1answer
164 views

oversampling in nested cross validation

Introduction I have a small mixed dataset consisting of continuous and categorical independent variables with a dichotomous dependant variable. I'm running various algorithms (neural networks, random ...
5
votes
1answer
463 views

During oversampling of rare events, why are the beta coefficients of the independent variables not affected, but only the intercept?

I have followed the King and Zeng paper and understand the consistency of the prior correction after oversampling in logistic regression. But I am trying to understand why the beta coefficients of the ...
1
vote
0answers
56 views

The problem between over-sampled, down-sampled data and original data using SVM with unbalanced data

I am working on a classifier that is supposed to do binary classification on a dataset includes 5996 examples. ~800 of this examples belongs to class 1 and the rest is class 0. Since there is a huge ...