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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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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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
51 views

Class balancing of the dataset

While performing the SMOTE for balancing the class data, what should be the proportion of both class? For instance, if we have 100 instances, what (%) should be the Yes class and what should be the No ...
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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
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 ...
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2answers
6k views

Why class weight is outperforming oversampling?

I am applying both class_weight and oversampling (SMOTE) techniques on a multiclass classification problem and getting better results when using the class_weight technique. Could someone please ...
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2answers
96 views

Forcing class imbalance to mirror the target data

I'm trying to do binary classification on some data, my source data has a class split of 40% A / 60% B while my target data has a split of 70% A / 30% B. Is it a worthwhile strategy to use SMOTE to ...
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0answers
103 views

Deep learning(MLP) on multiclass classification. Model learns only one class

I am new to deep learning. I have imbalanced class data. I used one hot encoding and scaling to preprocess my data. I have used adamoptimizer as optimizer function and sparse categorical crossentropy ...
3
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1answer
2k views

SVM SMOTE fit_resample() function runs forever with no result

Problem fit_resample(X,y) is taking too long to complete execution for 2million rows. Dataset specifications I have a labeled dataset about network features, ...
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1answer
3k views

Using SMOTE for Synthetic Data generation to improve performance on unbalanced data

I presently have a dataset with 21392 samples, of which, 16948 belong to the majority class (class A) and the remaining 4444 belong to the minority class (class B). I am presently using SMOTE (...
3
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1answer
741 views

Should I oversample my validation data to get better F1 score and PRC?

I am currently working with a dataset that is imbalanced, about 30k rows * 14 features (just for you know), and 99.5% of the data is labeled 0. Since the model is strongly imbalanced I decided to use ...
3
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1answer
3k views

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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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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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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0answers
21 views

SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?

As per the paper on SmoteBoost, SMOTE is ran for each iteration of the boosting, generating N samples, which are further added to the original training data and the weight distribution of the ...
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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
224 views

Increase Specificity of a model using SMOTE arguments from DMwR package in R when training data is unbalanced

I'm working on a binary classification problem and training data which I'm using is unbalanced. I used SMOTE function from DMwR ...
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2answers
173 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 ...
2
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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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0answers
110 views

A classification machine learning flow chart implimenting dimentionality reduction, upsampling, and cross validation [closed]

I would like to make a flow chart for an ML classifier and make sure that my thinking is correct. Here is a little about my sample: I have 3 classes and about 160 features. I suspect that some of ...
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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 ...
0
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1answer
726 views

Vertical and horizontal lines appearing on large confusion matrix?

I have produced a large heatmap-like confusion matrix and am seeing horizontal and vertical lines on it, so I'm trying to determine: What they mean Why they are there How I can improve on this ...
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1answer
2k views

How to avoid resampling part of pipeline on test data (imblearn package, SMOTE)

I am using the imblearn package to resample some data before applying other transformation/prediction techniques. Specfically, I am using SMOTE in a slightly unconventional way, as a data ...
2
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0answers
451 views

DBSMOTE on Short Text Classification

I am trying to use DBSMOTE(Density-Based Synthetic Oversampling TEqnique) to on a data set of short text--tweets to be specific. This will be used to train a classifier model in a multiclass ...
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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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3answers
19k views

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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1answer
197 views

suggestion to implement undersample and oversample

My dataset has the following class distribution ...
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1answer
2k views

Logic behind SMOTE-NC?

In the SMOTE paper here, the authors present the logic for creating synthetic examples when some of the features are nominal and some are continuous (section 6.1, SMOTE-NC). This example is provided: ...
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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 ...
5
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3answers
27k views

SMOTE and multi class oversampling

I have read that the SMOTE package is implemented for binary classification. In the case of n classes, it creates additional examples for the smallest class. Can I balance all the classes by running ...
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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 (...
15
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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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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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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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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 ...
2
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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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