Questions tagged [smote]

Synthetic Minority Oversampling Technique (SMOTE) is an approach used for dealing with imbalanced datasets before running them through machine learning models.

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1answer
197 views

suggestion to implement undersample and oversample

My dataset has the following class distribution ...
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0answers
9 views

Oversampling techniques for a class with 1 sample

I have 5 classes, one of them having only one sample. I've been researching techniques to oversample such as SMOTE and Bootstrapping but they do not work for the class with only one sample. I am ...
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1answer
51 views

Train score is very lower than Test score, is that normal?

I am working on very imbalanced dataset, I used SMOTEENN (SMOTE+ENN) to rebalance it, the following test is made using Random Forest Classifier : My train and Test ...
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3answers
14k views

Train/Test Split after performing SMOTE

I am dealing with a highly unbalanced dataset so I used SMOTE to resample it. After SMOTE resampling, I split the resampled dataset into training/test sets using the training set to build a model and ...
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3answers
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How do you apply SMOTE on text classification?

Synthetic Minority Oversampling Technique (SMOTE) is an oversampling technique used in an imbalanced dataset problem. So far I have an idea how to apply it on generic, structured data. But is it ...
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2answers
2k views

solving multi-class imbalance classification using smote and OSS

I am trying to solve multi-class imbalance classification problem for that i am using SMOTE for oversampling and OSS for under-sampling. But I have a doubt as I am working on multi-class so i have to ...
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1answer
104 views

Preferred approaches for imbalanced data

I am building a binary classification model with imbalanced target variable (13% Class 1 vs 87% class 0). I am considering the following three options to handle the data imbalance Option1: Create a ...
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0answers
14 views

SMOTE for Image regression?

Can you use something like SMOTE for an image regression task, where the target value is very skewed and imbalanced? I already tried using classic augmentation techniques like flipping, cropping, etc. ...
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22 views

Training data with one class

I have a real-time scenario, finding out whether a transaction is fraudulent or not. I have a dataset that contains only fraudulent transactions. For any binary classification algorithm, we may need ...
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3answers
47 views

Confusion Matrix before and after SMOTE is same

I am working with a very unbalanced dataset and I used SMOTE (for training data only). However, I did not understand why the results before and after SMOTE are the same. The attached confusion matrix ...
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0answers
29 views

How to use SMOTE to rebalance multiclass dataset when the target is one hot encoded with pd.get_dummies?

I'm using a multiclass dataset (cic-ids-2017), which is very imbalanced. I have already encoded the categorical feature (which is the target) using ...
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1answer
75 views

Methods for augmenting binary datasets

I have a small (~100 samples) dataset with roughly 20 features which are mostly binary, and a few are numeric (~5). I wanted to use methods for augmenting the training set and see if I can get better ...
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0answers
36 views

Train/ Test split on small dataset along with SMOTE

I have a binary classification imbalanced dataset with 1000 samples ( 15% of class 1, 85% of the rest). My main goal is to build a robust classifier using the following approach. Wanted to know if ...
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1answer
547 views

Optimizing decision threshold on model with oversampled/imbalanced data

I'm working on developing a model with a highly imbalanced dataset (0.7% Minority class). To remedy the imbalance, I was going to oversample using algorithms from imbalanced-learn library. I had a ...
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1answer
102 views

Unbalanced data set - how to optimize hyperparams via grid search?

I would like to optimize the hyperparameters C and Gamma of an SVC by using grid search for an unbalanced data set. So far I have used class_weights='balanced' and selected the best hyperparameters ...
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2answers
24 views

How to properly use oversampling without inflating results?

I am using with a tiny private dataset (over 192 samples) with 4 classes. A preprocessing step is trivial in order to do any classification. Among feature selection and extraction techniques, i ...
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1answer
135 views

How to use SMOTE in Stacking in SKLearn?

I have a data set X,y and split them to train and test data. ...
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1answer
139 views

Noise Elimination with majority vote filtering

I have a dataset with label noise which I wan't to clean with majority/consensus vote filtering. This will mean I will divide the data in K-Folds and train an ensemble model. Than using the ...
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1answer
156 views

Is it good practice to use SMOTE when you have a data set that has imbalanced classes when using BERT model for text classification?

I had a question related to SMOTE. If you have a data set that is imbalanced, is it correct to use SMOTE when you are using BERT? I believe I read somewhere that you do not need to do this since BERT ...
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18 views

How to create training dataset based on sampled or original data?

I am trying to used SMOTE and Feature Selection by following this paper http://jad.shahroodut.ac.ir/article_825_679b8f128dec2874a8fbc314fc922127.pdf In this paper, the authors have mentioned about 4 ...
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2answers
118 views

Main options on how to deal with imbalanced data

As far as I can tell, broadly speaking, there are three ways of dealing with binary imbalanced datasets: Option 1: Create k-fold Cross-Validation samples randomly (or even better create k-fold ...
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1answer
23 views

Looking for binary class datasets with high class imbalance, that also have intra-class imbalance in the minority class

Newbie question alert... For a college project I want to compare a few variants of SMOTE in terms of how much they improve classification of the minority class, over using random oversampling. I have ...
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0answers
258 views

Imblanced-data: Need assistance with SMOTE technique for a CNN input

I am trying to apply the SMOTE sampling technique to over-sample the minority class of a multiclass (5-class) problem using the convolutional neural network. As far CNN requirement, the input shape ...
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0answers
356 views

Balancing the dataset using imblearn undersampling, oversampling and combine?

I have the imbalanced dataset: data['Class'].value_counts() Out[22]: 0 137757 1 4905 Name: Class, dtype: int64 ...
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1answer
106 views

Why removing rows with NA values from the majority class improves model performance

I have an imbalanced dataset like so: df['y'].value_counts(normalize=True) * 100 ...
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2answers
2k views

SMOTE for multilabel classification

I have a dataset with 77 different labels. Each sample has one or more of these labels. I did some data analysis and found out that the dataset is highly imbalanced - there are a large number of ...
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0answers
137 views

How do I handle class imbalance for text data when using pretrained models like BERT?

I have a skewed dataset consisting of samples of the form: Category 1 10000 Category 2 2000 Category 3 400 Category 4 300 Category 5 100 The dataset ...
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0answers
34 views

Follow up question regarding Upsampling for Imbalanced Data and the use of ADASYN instead of SMOTE

I have a follow-up question regarding this topic. I have been working on a project predicting success(1) or failure(0) for organizations by using the Decision Tree and Random Forest algorithms. My ...
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1answer
734 views

Why you shouldn't upsample before cross validation

I have an imbalanced dataset and I am trying different methods to address the data imbalance. I found this article that explains the correct way to cross validate when oversampling data using SMOTE ...
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52 views

Overfitting in imbalanced dataset

I am working on a dataset related to an insurance company and the objective is to predict if the insurance buyer will claim their travel insurance or not. Training data: https://raw.githubusercontent....
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1answer
1k views

Scripting code for class imbalance in Biolabs Orange

I'm trying to manipulate some data in Biolabs Orange, using the built in Python Script widget and information at Biolabs Orange tutorial on scripting. However, I'm struggling with taking the results ...
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1answer
172 views

SMOTE and oversampling with constraints

I'm trying to apply SMOTE to a dataset that has time-constraints. I have information about users visiting a website. For some features, there are time constraints, e.g having the first visit and the ...
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0answers
20 views

How many instances should be synthesized for each class when using over-sampling techniques?

As for an imbalanced multi-class dataset, how many instances should be synthesized for each class if we use over-sampling techniques such as SMOTE? For example, there is 4 class including 'A', 'B', 'C'...
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1answer
164 views

SMOTE train test split with validation data [duplicate]

Would like to ask, in which way to use SMOTE? My dataset is imbalanced and a multiclass problem. As I read in many posts, use SMOTE method only for the training dataset (X_train and y_train). Not for ...
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1answer
46 views

Model to choose with Cross Validation or not?

I made different tests on an imbalanced dataset and got these results: Model 1 = train test validation split + Cross Validation(cv=10) --> f1'micro' 0,95 Model 2 = train test split + smote method ...
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2answers
4k views

Oversampling/Undersampling only train set only or both train and validation set

I am working on a dataset with class imbalance problem. Now, I know one needs to oversample or undersample only the train set and not the test set. But my issue is: whether to oversample the train set ...
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2answers
2k views

How do I run SMOTE on image data using the packages available?

I need to balance some image datasets, how do I use SMOTE variants or the imblearn SMOTE method with images? I can't figure out how to, since they seem to be working only with numerical datasets.
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0answers
354 views

How to apply oversampling when doing Leave-One-Group-Out cross validation?

I am working on an imbalanced data for classification and I tried to use SMOTE previously to oversampling the training data. However, this time I think I need to use a leave-on group out (LOGO) cross-...
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0answers
50 views

Different result between Rapidminer and Python imblearn

I'm currently working on imbalanced classification problem. However i found different result between SMOTE in rapidminer and SMOTE in imblearn (python). rapidminer SMOTE give 15-20% improvement on ...
1
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1answer
19 views

SMOTE for multi-instance learning i.e num_rows(x_train) > num_rows(y_train)

I have an imbalanced dataset and I wish to predict classes(0 or 1). Sample x_train: ...
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1answer
1k views

What is the best performance metric used in balancing dataset using SMOTE technique

I used smote technique to oversample my dataset and now I have a balanced dataset. The problem I faced is that the performance metrics; precision, recall, f1 measure, accuracy in the imbalanced ...
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1answer
233 views

How to perform SMOTE-N when there is no majority vote?

In the SMOTE paper, the authors present the logic of creating synthetic examples when all features are nominal (section 6.2, SMOTE-N): To generate new minority class feature vectors, we can create ...
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0answers
985 views

Can SMOTE be applied over sequence of words (sentences)?

I have a highly unbalanced text classification data. I am trying to over-sample through SMOTE. I have a doubt that applying SMOTE over sequence of word indices will give me valid data points or not (...
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2answers
1k views

How to find nearest neighbors in SMOTE

I am reading the original paper by Chawla and others for SMOTE. I am trying to understand how to generate these synthetic examples for over-sampling the minority class. The paper says: "Synthetic ...
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0answers
41 views

SMOTE caused my total nrows in train to fall to a very small proportion

I have a highly skewed dataset with minority class in target being just about 4%. I decided to apply SMOTE using library DMwR in R. Here is my target: ...
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1answer
1k views

SMOTE and standardisation

I have an unbalanced dataset X. I split it between data and labels, then I standardize the data. Then I use train_test_split to split between train and test data and I output the result. Now I want ...
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1answer
182 views

Variation in output of Logistic Regression when using SMOTE

I am working on a logistic regression case with an imbalance in the target variable. To fix this I am using SMOTE (Synthetic Minority Oversampling Technique), but each time I run my regression model, ...
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0answers
735 views

class imbalance - applied SMOTE - next steps

I am new to ML and learnt a lot from your valuable posts. I need your advise with the following situation and guidance on if the steps make sense. I have a binary classification problem, my dataset ...
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1answer
98 views

On which step should use SMOTE technique for over sampling?

I want to use SMOTE technique for over sampling but I don't know on which step on pre-processing I should use it. My preprocessing steps are: Missing values Removing Outliers Smoothing Data Should ...
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2answers
511 views

location of the resampled data from SMOTE

I am using SMOTE in Python to perform oversampling of the minor class in an unbalanced dataset. I would like to know the way SMOTE formats its output, that is, whether SMOTE concatenates the newly ...