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Questions tagged [smote]

SMOTE stands for "Synthetic Minority Over-sampling Technique". It is a method to deal with imbalanced data.

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SMOTE sampling for multi-class data

I have a classification problem with 4 distinct classes, but imbalanced data. ...
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25 views

SMOTE algorithm gives better AUC than matching

I have a highly imbalanced data set: a total of 13000 patients, 160 having condition A, and various other features which could be predictors. In order to balance the data I did two things: 1) ...
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SMOTE and ROSE methods in CARET (R) do not work for balancing a multi-class dataset

SMOTE and ROSE methods in CARET (R) do not work for balancing a multi-class data set. Specifically, ROSE throws an error telling that it needs two levels. It means that it does not work for multi-...
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PCA, SMOTE and cross validation - how to combine them together?

I was reading a lot recently about PCA and cross validation and it seems that the majority call it malpractice to do PCA before cross validation. I would also like to perform SMOTE, but there is a ...
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175 views

Python / Keras: SMOTE and validation_split

I try to train a MLP with an imbalanced dataset. I'd like to use SMOTE to balance my classes; as highlighted here (https://beckernick.github.io/oversampling-modeling/), the class rebalancing should ...
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52 views

SMOTE - What is the difference in sampling before or inside train() [closed]

I have an unbalanced dataset and would like to apply SMOTE to the training data. I can either do one of the following: Inside trainControl() add ...
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477 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 ...
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29 views

Can SMOTE Be Used for Pure Data Augmentation and not Just Imblanaced Classes

I have learned that SMOTE can be used to deal with imbalanced class datasets. Could it also be used to create a larger dataset, preserving the original structure/distribution and thus also the ...
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For ADASYN, if the neighbourhood of a minority sample contains no other minority sample, do I double the sample?

In ADASYN, for the last step in the paper linked below, if there exists no other minority class in the k-NN other than the one minority example, do we simply just double the training example? Because ...
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239 views

Reverse CARET proProcess()

I did the following steps in my modeling using R: 1)applied preProcess(data, method = c("bagImpute")) function in CARET package and then encoded the data. 2)Used SMOTE to balance the data(because the ...
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1answer
62 views

t-test or paired t-test to detect drift for suicide prediction

Context and data I am studying suicides among the military. I created a table that aggregates certain metrics (number of holidays, number of hours worked, etc...) for each officer, for each month ...
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1answer
79 views

Train on balanced datasets, used for imbalanced datasets?

We usually trained a model using balanced datasets. Even when we do not have a balanced datasets, we will use methods such as SMOTE to create a balanced dataset for training. The question is - how ...
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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 ...
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22 views

Resampling imbalanced data in a multi-view scenario

Assume a multi-view scenario, where multiple views of the same entity are available. If each data pair is assigned a label and the resulting scenario is highly imbalanced, what are proper ways of ...
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1answer
22 views

Use of SMOTE with training, test and dev sets

I have a dataset with class imbalance about (1:10). I applied a SMOTE method by slitting the dataset into training and dev. I over sampled the training set using a SMOTE method and divided this set ...
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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 ...
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718 views

SMOTE data balance - before or during Cross-Validation

I'm using Random Forest in the CARET package to tag a binary outcome with 1/10 ratio, thus I need to balance the dataset. I know two ways: Use SMOTE as a stand-alone function and then pass it to the ...
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642 views

Why does SMOTE over-sampling improve my accuracy? [closed]

I have an imbalanced dataset, for which I first split my data into train and test and then apply SMOTE on train data only. When I run the model on original test data (my test set is also imbalanced), ...
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391 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 ...
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158 views

Applying SMOTE and increasing sensitivity

I am trying to analyze lending club data and want to predict whether a loan is risky or safe using random forest with decision tree as a classifier. The data is imbalanced. It contains one-fourth of ...
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9k views

ROSE and SMOTE oversampling methods

Can somebody give me a brief explanation of the differences between those two resampling methods : ROSE and SMOTE ?