Questions tagged [class-imbalance]
The class-imbalance tag has no usage guidance.
122
questions
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What are the standard parameters values for SMOTE technique?
I'm working on an imbalanced class data set (200 samples) with 2 classes, first class has 50 sample and second has 150 sample.
My questions:
When I use SMOTE technique on my data set my total ...
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19 views
How to deal with time-series imbalance classification data?
I want to predict the user to buy or not a product in next month in the e-comercial site. I mainly using the past 1-year data to predict it. But I found the training data is imbalanced, and the buy(1)...
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1answer
26 views
How much is the Class Imbalance Problem rates?
I'm working on a data set and wanted to know is there a standard rate about Class Imbalance problem or not?
I have 47 samples in Class A and 150 Sample in class B , should I use Class Imbalance ...
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2answers
27 views
Does Sampling size matters in Multi classification Model
I am working on a multi class classification model where few of the class are with less data compare to other classes.
I used random sampling technique to create a sample from the population keeping ...
1
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1answer
14 views
Adjust predicted probability after smote
i have an imbalance data set and I used smote to oversample the minority class and undersample the majority class. now, I want to check the test AUC using predict_proba of the model.
I have two ...
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0answers
29 views
Data Augmentation techniques for classification of imbalanced time series datasets
Now I have a task to classify the imbalanced time series datasets using ML classifiers, such as Logistic Regression, Decision Tree, SVM, and KNN. I am not allowed to use the Deep Learning tools, such ...
0
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1answer
21 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.
1
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1answer
14 views
Balancing data-sets for regression problems
Unbalanced data-sets are a well described problem for classification-problems.
However, for regression similar problems can arise. An example is the data-set where target variable has a very ...
2
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2answers
30 views
model predicting probability close to 50 for positive classes in imbalanced training dataset
I have a binary classification model where I am predicting the positive class which is only 10% of whole training data set. The issue with this imbalanced data set is my model is predicting ...
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0answers
10 views
Class balancing in Weka
Hi I am using software fault prediction datasets, which has class values Yes (faulty) and No (non faulty). I have to do class balancing at ratio of 20-80% (faulty=20, non faulty=80).
So for that,do I ...
0
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2answers
39 views
Imbalanced dataset - Undersampling & multiple classifiers [duplicate]
Let's suppose that my dataset in a classification problem looks like that:
class A: 50000 observations
class B: 2000 observations
class C: 800 observations
class D: 200 observations
These are some ...
1
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2answers
67 views
Which scoring for GridSearchCV is best, when imbalanced multiclass dataset?
I have an unbalanced multiclass dataset (GTSRB) and want to optimize the hyperparameters of an SVM through GridSearchCV.
I know that accuracy is not suitable for scoring in this case.
Which evaluation ...
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0answers
16 views
Stratified sampling for large imbalanced data set
I have a very large data set (64 Million Rows) and I want to understand the best approach to sample the data for explorative data analysis before proceeding to perform any classification modeling.
...
0
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2answers
60 views
How to find whether a dataset is blanced or imbalanced?
I have few dataset to experiment classification(Multi-class). These datasets are about 400GB. I wanted to know whether the dataset is balanced or imbalanced. How to know that dataset is balance or ...
0
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3answers
110 views
How to Split And Resample Imbalanced Dataset Into Train, Validation and Test
I want to understand how to split the imbalanced data set with a binary target variable where 87% of the samples are negative and 13% of the samples are positive. Now, I know that you should always ...
1
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1answer
26 views
How to control the amount of positives in classification?
I have a basic, yet quite complex problem to solve right now. Let's say we have a training set of 20,000 samples in my training set, out of which 3 to 4% is flagged as "True", the rest is flagged as "...
2
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3answers
42 views
Highly Imbalanced dataset fro classes more than 200
I have a text dataset where I need to train a classifier to classify the titles into categories. The dataset shape is more than 575000. There are 256 target classes here. The problem is the dataset is ...
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3answers
100 views
Why did sampling boost the performance of my model?
I have an imbalanced dataset with 88 positive samples and 128575 negative samples. I was reluctant to over/undersample the data since it's a biological dataset and I didn't want to introduce synthetic ...
0
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0answers
16 views
Clustering in python when imbalanced data sets exist
I have a set of measurements with four features.
Two features are continuous (time and distance) and two are discrete.
We also know that the population consists of two groups. One is the minority ...
0
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1answer
96 views
SMOTE vs SMOTENC for binary classifier with categorical and numeric data
I have a problem that I am having trouble thoroughly understanding.
I am using Xgboost for classification. My y is 0 or 1 (true or false). I have categorical and numeric features, so theoretically, I ...
0
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1answer
22 views
Why does downsampling leads classification to only predict one class?
I have a multi-class classification problem. It performs quite well but on the least represented classes it doesn't. Indeed, here is the distribution :
And here are the classification results of my ...
1
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2answers
1k views
Weighted Binary Cross Entropy Loss — Keras Implementation
I have a binary segmentation problem with highly imbalanced data such that there are almost 60 class zero samples for every class one sample. To address this issue, I coded a simple weighted binary ...
0
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1answer
43 views
Evaluate imbalanced classification model on balanced testing sample
Why it would be too optimistic to compute presicion, recall and f1-score to evaluate a model trained for imbalanced classification on a balanced testing sample ?
3
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2answers
136 views
Is There a Way to Re-Calibrate Predicted Probabilities After Using Class Weights?
I have classification data with far more negative instances than positive instances. I have used class weights in my models and have achieved the discrimination I want but the predicted probabilities ...
0
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1answer
38 views
Combining 'class_weight' with SMOTE
This might sound a weird question, but I could not find enough details in sklearn documentation about 'class_weight'. Can we first oversample the dataset using SMOTE and then call the classifier with ...
0
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1answer
67 views
Choice of f1 score for highly imbalanced dataset?
I am confused whether to use f1 score with 'micro' average or 'macro' average for better evaluation.
Given my dataset is highly imbalanced(600:100000)
2
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1answer
104 views
Difference between sklearn make_pipeline and imblearn make_pipeline
Can anybody please explain the difference between sklearn.pipeline.make_pipline and imblearn.pipeline.make_pipline.
1
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1answer
81 views
Oversampling only balances the training set, what about the testing set?
In a case of imbalanced data classification, I know that we only oversample the training set (to prevent data leakage from training to testing subsets), but what if there are no positive data points ...
2
votes
2answers
73 views
Resampling for imbalaced datasets: should testing set also be resampled?
Apologies for what is probably a basic question but I have not been able to find a definitive answer either in the literature or in the Internet.
When dealing with an imbalanced dataset one possible ...
5
votes
2answers
58 views
Can we specify the number of data generated(minority class) using SMOTE?
I am trying to improve classification of imbalanced dataset creditcard fraud using SMOTE imbalanced_learn. But, in this it generates the data to 50%, can we give a specific number for the data to be ...
1
vote
1answer
132 views
ROC AUC score is better if test data is imbalanced
I have an imbalanced dataset and I'm using XGBoost to do binary classification. I used down sampling together with target and one hot encoding for train data. For ...
0
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0answers
27 views
GridSearch on imbalanced multi-class dataset
I have an imbalanced multi-class dataset (GTSRB) and would like to use GridSearch to determine the hyperparameters for an SVM. As metric for the evaluation I chose F1 with average macro.
...
1
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1answer
110 views
Choosing weights on random forest for imbalanced data with the aim to minimize false positives
I am currently dealing with a binary classification task on imbalanced data with the following distribution:
...
1
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1answer
149 views
Poor performance of regression model for imbalanced data
I am trying to train a neural network model to solve a regression problem. The specificity of my dataset is that it has something like an exponential distribution of target values (imbalanced). ...
2
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1answer
261 views
How does class_weight work in Decision Tree
The scikit-learn implementation of DecisionTreeClassifier has a parameter as class_weight.
...
3
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1answer
58 views
Machine Learning: Balanced training set but highly unbalanced prediction set? How to adjust?
I am trying to train a model to detect gender in a dataset of CEO speeches. Here are the datasets that I have:
Final Dataset: 20K CEO voices analyzed (around 95% male)
Testing dataset (?): 1K CEO ...
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0answers
67 views
Best strategy to build Classifier for Mixed Data with class imbalance
I have a dataset which contains : 94 numeric features + 56 categorical features
I am trying to build a classifier to predict Target (disease/healthy).
2. Rows : 1812
3. Class imbalance ( Majority ...
0
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0answers
13 views
How to identify whether or not my dataset is sufficient enough to be learnt from any model at all?
I created my own bipartite dataset where 1 group consists of disease and another group consists of genes.
The number of diseases is significantly lower that number of genes. Furthermore, one disease ...
0
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2answers
41 views
Low prediction/classification accuracy due to imbalance in data feeding
I am building the neural network for image analysis to do Chest Xray classification (Abnormal/Pass).
The classification accuracy for abnormal Xray is low, I guess it is due to the lack of abnormal ...
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0answers
29 views
Problem On Class Imbalanced Data
I am getting an F-Score of 0.99 on the train_test_split data, but only getting 0.40 for a competition's test data. I am oversampling with random forest (after ...
3
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1answer
44 views
Which classification algorithms are negatively affected by class imbalances?
I've seen a few posts and papers floating around the web (mostly those related to over/undersampling, SMOTE, and cost-sensitive training) that, when discussing class imbalance, specify that certain ...
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2answers
87 views
Clustering on imbalanced data that has high correlation
I am clustering images of two categories, but for the purposes of the experiment, I do not know the labels i.e. this is an unsupervised problem. Via ...
0
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0answers
62 views
Metrics parameter in model.compile function in Keras for imbalanced data set
Accuracy for an imbalanced data set is not relevant and therefore I use precision and recall to evaluate my model. However, whenever I train a model in Keras a metrics parameter must be specified in ...
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0answers
28 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 ...
0
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2answers
56 views
A robust metric in the presence of class imbalance
When evaluating the performance of a multiclass classification problem, on a highly imbalanced dataset, what is the most robust metric for this purpose?
I read a paper that states:
"Average ...
2
votes
2answers
687 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 ...
0
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0answers
45 views
Why does Logistic Regression perform better than Autoencoders when classifying imbalanced data?
The 'shuttle' data can be downloaded from the link here.
It is imbalanced data and there are two classes in the target variable. The proportion of the two classes are seven percent. I used Logistic ...
0
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2answers
68 views
Dealing with the test set of imbalanced data
I am working on a problem dealing with unbalanced data that has a very specific request.
I would like to know the following:
When I have an imbalanced dataset and I do train test split, the test ...
0
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1answer
22 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 ...
6
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1answer
219 views
Imbalanced classes (balance of train, validation, and test)
1) I am currently trying to set up a feedforward neural network with highly imbalanced classes (binary classification) in which the number of observations of class 1 is very low (and the class of ...
