Questions tagged [class-imbalance]

Questions referring to classifiers or classifying problems where some of the classes in the data are under-represented.

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Recall/Precision Metrics on Azure AutoML seem to be oriented to majority class, and I'm trying to focus on minority class

I am running some experiments in Azure using AutoML. My problem is a binary classification one, with highly imbalanced classes (basically trying to predict what factors make a deal "WON" ...
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16 views

Feature Engineering on 3 dimensions data

I'm doing a task where I was given 3 features (a1, a2 and a3) and 3 heavily unbalanced classes. I tried many balancing techniques like SMOTE and undersampling. None of them gives me a reasonable ...
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10 views

Implementing class weighting in Faster RCNN

I have a dataset (around 45,000 screenshots) of UI elements (UI trees containing element types and bounding boxes) and associated screenshots: The dataset is highly imbalanced with the button element ...
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1answer
38 views

Should I resample my dataset?

The dataset that I have is some text data consisting of path names. I am using TF-IDF vectorizer and decision trees. The classes in my dataset are severely imbalanced. There are a few big classes with ...
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17 views

Dependent variable with very few distinct discrete values

I've been reading about different ways to produce models for a dependent variable that has very few distinct discrete values, but haven't found the right fit. I was thinking of using an ordinal logit ...
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11 views

Is there any benchmark dataset for unbalanced text classification?

I want to work on an unbalanced dataset for text classification (sentiment analysis, intent classifier) and hopefully, come up with an idea to improve the classification on such datasets. Is there any ...
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Account for imbalanced data in a Neural Network using prior distribution

I have a dataset with 4 classes, say their distribution in the training-set is $P_{prior}(C1) = 60\% $ $P_{prior}(C2) = 25\% $ $P_{prior}(C3) = 10\% $ $P_{prior}(C4) = 5\% $ After training a Neural ...
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51 views

Binary Classification with Imbalanced Target [closed]

I have a dataset and my objective is to run a Binary Classification, but my target feature, that is supposed to have "True" and "False", only has "True", as a value. I ...
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1answer
27 views

Evaluation metric for imbalanced data

Hi I'm a CS graduate student I have a question for AI or data experts. I'm writing a paper My dataset is time-series sensor data and anomaly (positive class) ratio is between 5% and 6% you can see the ...
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26 views

What do you suggest to increase the sensitivity rates in conventional ML model?

I have an imbalanced data problem (prop. rate: 0.8571429 0.1428571) and for this reason, our sensitivity and PPV rates are very low. What do you recommend to fix this problem in R or in general? See ...
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22 views

How to handle imbalanced NLP text data set e.g. some classes only have 2 records

I am working on a dataset with around 2000 records. Around 80% records have their the categorical labels. There are around 200 categories, some categories got more than 20 records; whereas others only ...
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19 views

How to apply class weights for imbalanced data and given misclassification costs?

Say I have a dataset for binary classification with 1000 samples in the minority class and 100000 samples in the majority class and use XGBoost to output probabilities. From an expert I know that ...
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How to pass manually split data to cross-validation

I have to perform a binary classification. My dataset is quite small 280 samples and quite imbalanced (1:10 ratio). I kept around 100 sample as testing and about 140 for training. My input variables ...
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Is it right method to remove instances that are hard to predict before train test split?

In a binary classification problem, I have a slightly unbalanced medical dataset with class distribution: 0:5600, 1:1500 0 without a problem and 1 with a problem. I tried many pipelines, automls, and ...
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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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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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110 views

Imbalanced classification with bias

The problem: A business historical heuristic rule for offering a special deal to customers has created a bias in the dataset when trying to use machine learning in order to make a more sophisticated ...
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1answer
26 views

On what threshold we should resample the data?

When working with churn datasets, we usually find imbalanced datasets. My question is how to decide on what basis we should resample the data. For example: while splitting the data before training we ...
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Is balancing class data for imbalanced problems helpful or just folklore when considering thresholds?

Caveat: I'm aware that imbalanced data questions are a dead horse, but I haven't found an answer to this flavor of it directly. When working with highly imbalanced data (e.g. binary class cases), ...
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16 views

Evaluating a model with different target class distributions between training and testing data

I'm having a bit of an argument about when class imbalances matter when training a classifier, so I was hoping to get some help on understanding a specific concept. Say I have a problem where I want ...
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30 views

XGBoost failing on highly imbalanced data!

I am working on a classification problem, where I am trying to predict a fraud login. The data is highly imbalanced i.e. 0 = non fraud logins , 1 = fraud logins 0 : 4538076 1 : 365 I have been trying ...
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How to optimize AUPRC for imbalanced data given a precision or recall bias?

My general understanding is that when optimizing a model in an imbalanced class case with a small preferred target class one should optimize first for a model with the best AUPRC (assuming one doesn't ...
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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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Imbalance classes in Named Entity Recognition

I am currently working on a NER problem which attempts to extract 2 entities - place-of-interest(POI) and street from an address string in the Indonesian language. I used IndoBert (available here) and ...
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1answer
39 views

Training is not stable with extreme class imbalance

I'm dealing with a multi-class classification problem with around 30 categories. This problem has a severe class imbalance: Around 300 examples for the least common class. Around 100k examples for ...
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18 views

Using class weight in decision trees with Information Gain

How are weights considered in a decision tree when we want to maximize Information Gain? In other words, what would the entropy calculation become when weights are involved? I can guess either $$ e_1 =...
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1answer
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Data simulation using make_classification in Python

I have a question about data simulation in Python. I deal with the classification of imbalanced data and want to test the effectiveness of different methods on simulated data. I have seen in various ...
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Computing class_weights using native tf.Dataset

There are already some answers on DSE about computing class-weights here but they require you to already know the number of samples in each class and assume it is ...
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1answer
34 views

class weights formula for imbalanced dataset

I am trying to make some semantic segmentation. I have 7 imbalanced classes in my case. I found several methods for handling Class Imbalance in a dataset is to perform Undersampling for the Majority ...
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2answers
39 views

Doesn't over(/under)sampling an imbalanced dataset cause issues?

I'm reading a lot about how to use different metrics specifically for imbalanced datasets (e.g. two classes present, but 80% of the data is one class) and how to tackle the issue of imbalanced ...
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Imbalanced data identification with Shannon entropy

I was studying about the imbalanced data. Then I had a question , how would someone know that which data is imbalanced or not by looking at its percentage(20,30 or 40). Then I read an answer on stack ...
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1answer
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CNN unbalanced and small dataset

I would like to use CNN to make classification with 5 classes, but 4 of these classes only have between 16 and 60 images, while the last one has more than 1300. I know 16 or 60 images are not enough, ...
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20 views

scikit-learn sample weight interpretation

I'm trying to use scikit-learn to plot a confusion matrix from raw data I have obtained (contains just predictions and ground truths). The data contains a total of 4 classes: ...
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1answer
86 views

Logistic regression with unbalanced data, scoring based only on rare class

I have a dataset off app. 600.000 data points in which 0.2% (1.200 samples) is labelled as signifying a rare event. I want to use logistic regression to help me predict this rare event, but even when ...
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1answer
232 views

Specifying class or sample weights in Keras for one-hot encoded labels in a TF Dataset

I am trying to train an image classifier on an unbalanced training set. In order to cope with the class imbalance, I want either to weight the classes or the individual samples. Weighting the classes ...
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26 views

Can one still train a classifier with an unbalanced data set?

I want to train a binary Naive Bayes classifier. The problem is, is that I have an unbalanced set at my disposal, where the ration between the two classes is roughly 2:1 (250 examples from the first ...
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1answer
16 views

Unbalanced training set from balanced data

I am looking to get an unbalanced training set with a given ratio of classA:classB from a dataset without regarding if it is balanced or not. The point is to analyze the influence of data imbalance on ...
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2answers
132 views

High Recall but too low Precision result in imbalanced data

I was training a model using XGBoost Classifier on a heavy imbalanced database with 232:1 of binary class. Because my training data contains 750k rows and 320 features (after doing many feature ...
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2answers
38 views

Do we have any method which handles the imbalanced classification with sample weighting instead of class weighting?

I am looking for methods that use sample weighting instead of class weighting for the imbalanced classification. I think sample weighting is more precise than weighting all the samples from one class ...
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52 views

Oversampling Using the Orange Data Sampler Widget

It was pointed out in the help section of the Data Sampler widget that it could be used for under or oversampling. I used the Attrition dataset where the class imbalance is 1233/237. I separated the ...
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22 views

LightGBM model improvement when the focus is on probability prediction

I am building a binary classifier using LightGBM. The goal is not to predict the outcome as such, but rather to predict the probability of the target even. To be more specific, it's more about ranking ...
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How to train a deep neural network with time-series images and unbalanced dataset?

I have images that represent a fixed-length time-window of different serials. Serials have time-series of different size, so e.g. serial1 has length 30, serial2 length 110 and so on. I have multiple ...
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1answer
57 views

How to deal with class imbalance problem in natural language processing?

I am doing a NLP binary classification task, using Bert + softmax layer on top of it. The network uses cross-entropy loss. When the ratio of positive class to negative class is 1:1 or 1:2, the model ...
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Handle unbalanced data by implementing Edited nearest neighbors, smote and Tomek links in r?

Imbalanced data is a big problem in classification problems. I have a binary classification problem with imbalanced data. I have researched and found that a possible method of dealing with this is ...
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18 views

How to tune the proportion of minority class after oversampling in imbalanced data with cross-validation? [duplicate]

I have an imbalanced data set and I want to balance them with SMOTENC with cross-validation. In order to determine the performance of the classifier on the original data, I will cross-validation as ...
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1answer
42 views

How do I combine predictions from classifiers for two different problem?

I am working on a classification problem for predicting whether the shipment is going to be late or not. I would say the classifier is mediocre at predicting the positive class at the moment. But the ...
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80 views

What ML techniques work on imbalanced datasets

I have some specific questions for which I could not find answers in textbooks/research articles. Shall be grateful for an answer. These are: Are there ML techniques that can be directly applied on ...
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1answer
92 views

How does class_weight work in Decision Tree?

I am interested in Cost-Sensitive learning. And I am trying to understand how class_weight in DecisionTree works in terms of math. I read a lot of articles that ...
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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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1answer
56 views

Why does an imbalanced data set badly effect distance measures like Mahalanobis?

I'm relatively new to data science and I am struggling to understand why the Mahalanobis distance (or any other distance measure) applied to an imbalanced data-set becomes inaccurate. I have a data ...

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