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Questions tagged [multilabel]

Multi-label classification where multiple target labels might be assigned to each instance.

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Looking for a statistical test for significance of association between a treatment and non-mutually exclusive categories

I am looking for a statistical test which will give a p value for the association between a treatment and two potentially co-occurring labels: I have two categories of cells: state A and state B. A ...
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How should you split up data in a train-test-validation split

I've seen it is generally recommended when using a train-test-validation data split, to first split your data into train and test datasets, and then furtherly split the train dataset into a train and ...
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Duplicates with different labels in multi-label classification

In a multi-label classification problem there a duplicates in the training data that are labelled differently. For example, feature x is the same for both rows, ...
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Why is validation accuracy fluctuating between two extremes?

I am using a LSTM to make a multi-label classifier. However the problem is, the validation accuracy fluctuates between 0.7 and 0.3. I have about 4367 samples (80-20 validation split) and 240 sample ...
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Is Brier score strictly proper in multi-label problems?

In problems where one of $3+$ categories can be observed and we prodict the probability of each category being observed, it is known that the Brier score is a strictly proper scoring rule that is ...
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What can cause one label in a multi-label classification task to have the highest score most of the times?

I have a multi-label classification task in which I have 7 categories per each sample. I train a multi-label classification model (i.e., XLMRobertaForSequenceClassification). When I evaluate the model ...
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Ideal loss for a multi-label problem with soft targets

Given an input X, my goal is to predict a list of probabilities for n factors, where the factors could be attributes like ...
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How to encode multiclass target variable?

I have a ML project for classifying news articles. In my dataset I have a target variable called "category", which represents type of the article, ("IT", "Science & Tech&...
CraZyCoDer's user avatar
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How robust is multinomial logistic regression to having a multi-label problem shoehorned into it?

Consider a situation where there can be membership in group $A$, group $B$, both groups, or neither group. If we wanted to predict group membership probabilities from some covariate information, this ...
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Multi-label stratified split

I am working on a multilabel text classification problem. The text data is called 'cleaned_text' and has shape (92259, 1) and the one-hot encoded label data is called 'labels' and has shape (92259, 32)...
Steven Gubkin's user avatar
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Multilabel classification problem

I have a problem statement where I have two dataset one labeled where a data point can belong to only one class say class1 or class2 and there's another unlabeled dataset. Now for unlabeled dataset I ...
Sujit Kumar's user avatar
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Multilabel Classification Task using SVM

I want to classify diabetic retinopathy grades (normal, mild, moderate, severe, PDR) using SVM. But the problem is i don't know which type of svm should i use, because i extract three lession features ...
anastasia's user avatar
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Sampling strategies in multi-target classification

I am dealing with multi-target binary classifications (I have two targets). I need to use a sampling strategy. I have tried imblearn.pipeline but I'm getting the same error as this time when I'm ...
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Evaluating classifier with 2 labels and 'unknown' label

The classifier I'm using has 3 possible label outputs - POSITIVE, NEGATIVE or UNKNOWN. For training data, the labels are only POSITIVE and NEGATIVE. What is the best way to handle evaluating the ...
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Flipping inputs in multilabel classification

I have framed a classification problem as follows: I have $N$ items, and wish to predict a set of relevant tags for each out of $M$ tags. An item can have anywhere from 0 to $M$ applicable tags. To ...
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Multi-Label Classification where each label is a Multi-class problem

Problem: Currently, I have 15 classification models(multi-class + binary). Training and Maintaining 15 models take a huge time and cost. Also, I need to inference 15 models for every input. So I ...
Naren Babu R's user avatar
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Grouped stratified train-val-test split for a multilabel dataset

I was wondering if there is a fast heuristic algorithm for performing grouped stratified dataset split on a multilabel dataset. Question originally posted on Data Science stackexcahnge here. ...
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One text classification model, two sets of target classes

I am attempting to classify text sequences into two sets of labels - class_1 = [A, B, C, D], class_2 = [X, Y, Z]. The model will ...
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How do I know which class has been difficult to learn for my multi-class model?

For a multi-class model, there are always chances that the model is learning one class's features more than the other. But how do I find which class has been weakly learned? Please help.
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Multilabel Classification: Accuracy is very low. Metric or Model, which is inadequate?

In my multilabel classifaction problem, which I approach similarly to what can be see in this post: How does Keras handle multilabel classification?, the resulting accuracy only increases from 2% to 5%...
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What kind of architecture to use for non-binary output multi-label image clasification

I want to make a network for making multi-label attribute classifications on images of clothing. This is a simplified case of what I want to do, I have 9 different attribute categories that I wish to ...
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Bulding a labelling dataset and modelling categorical features

Summary: How do I ensure that sample I create for labelling is representative enough and would be appropriate for modelling, given I cannot include all feature combinations in it. I have a tabular ...
Dimitar Argirov's user avatar
7 votes
2 answers
305 views

What is the statistical model for a multi-label problem?

In a setting with a binary $y$ like dog/cat, a reasonable statistical model is to posit that the probability parameter $p$ of a $\text{Binomial}(1, 0)$ distribution is some function $f$ of features $X$...
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Using Hamming Loss for multi-label classification with imbalaned class and many labels

I'm working on a multi-label classification problem where I want to classify text into 20 categories, and each text may belong to one or multiple categories. Each category is a binary value of 0 or 1, ...
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Efficient method for tuning threshold in multi-label classification

Given a multi-label classifier, e.g. multi-label image segmentation, what are some typical strategies/libraries to help tune the class label threshold in order to maximize some metric, such as F1-...
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CNN for multi-class classification with occasional multi-labels

I have about 10 classes, on which I train a CNN with a softmax output layer using one-hot encoding and categorical cross-entropy loss. The problem is that two pairs of these of these classes (let's ...
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How to update multi-label classification prediction with extra information on the probability of a label occurring, given the other labels

I think this question is best expressed directly with an example. Let us say I have a picture of a dog in a lease in the park, and an algorithm for object recognition splits the following labels with ...
Carlos_San's user avatar
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Multi-label Classification without any training data

How can I perform multi-label classification without any training data i.e. just using candidate labels like zero-shot-learning? I was able to perform single label classification using only candidate ...
Ujjwal Karnani's user avatar
3 votes
1 answer
835 views

How to predict both category and sub category in machine learning classification?

There are 4 actions available. Each action has its own varying number of categories. The target is to predict an action along with the category of action, given input data. Assume actions are a,b,c,d ...
MrSpectre's user avatar
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what are the typical loss function for multi-label classification with dependent classes

For multi-label (multi-class) classification task, it seems to me that the standard loss to use is the binary cross entropy loss. However, it assumes that the the classes are independent and we are ...
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Multi-class multi-label with partial mutual exclusivity

Given an input, I want to predict 0/1 for each of N output classes. The output can be 1 for multiple classes. So I'm training with individual binary cross-entropies for each of the output classes. ...
user342018's user avatar
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Difference between Hamming Loss, Hamming Score, and Hamming Distance in multiclass multilabel classification

I am trying to understand the mathematical difference between, Hamming distance, Hamming Loss and Hamming score. I am trying to perform two actions Multiclass multi label classification using SVM K ...
Srinag Vinil Tummala's user avatar
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952 views

under sampling a multi-label dataset

I have a multi-label dataset, whose label distribution looks something like this, with label on x-axis and number of rows it occurs in the dataset in y-axis. ...
Naveen Reddy Marthala's user avatar
1 vote
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Multi-label classification where predicting any one label is fine

I am working on a problem with muti-label classification, where, in contrast to the conventional requirement that the correct prediction of each label is expected, we just need to predict ANY ONE of ...
Haochen Sun's user avatar
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How to get multiple outputs using classification techniques?

I want to predict roles based on technical skills column.I have column technical skills for ...
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Which model to use for multiple outputs in classification problem

I want to predict roles based on name, experience, soft skills, technical skills . Based on all these variables I want to ...
10sha25's user avatar
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1 answer
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how train AdaBoost M1 weak estimators?

I'm trying to implement AdaBoost.M1 as explained in Boosting: Foundations and Algorithms by Robert E. Schapire and Y. Freund. The problem is that I don't understand at each iteration t the estimator ...
abbassix's user avatar
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2 votes
1 answer
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How can I combine the scores of a multilabel classifier?

I have a keras neural network with 8 outputs and it is a multilabel problem, which means that an observation can be classified into more than one target class. Let's suppose I have the following ...
Matheus Nascimento's user avatar
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Am I doing correct unit test before whole batch training?

I read somewhere that unit tests are important before jumping onto training for the whole batch. And for that reason, if one sample overfits on the model, can we decisively say that the training will ...
banikr's user avatar
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1 answer
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effective labelling in multilabel classification

I am working on a multilabel classification problem with 44 features and 2 labels. Label2 is a binary (0,1) and Label1 had label encoding done on it up to ten (1,2...10). I did one-hot encoding on ...
Ahmed Anwer's user avatar
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Make use of wrong label in learning?

I have a dataset. Each sample has two labels. The labels in the first set are mostly correct (>90%). The other labels, say annotated by an inexperienced annotator, are mostly incorrect, but they ...
quasquas's user avatar
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One Multi-Label Classifier or Two Single-Label Classifier?

I have a dataset that each feature in a data could have two separate labels depending on separate definitions. According to definition 1, each feature could have one of two labels (A, B). According to ...
LCheng's user avatar
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1 vote
1 answer
695 views

Dealing with outliers and correlated features for a deep learning based classification problem

I am working on a multi-label multi-class classification problem that required me to use deep learning based approach. The data has around 17000 examples where each example has 42 numerical features ...
Meghal Darji's user avatar
1 vote
0 answers
695 views

Error "Feature names stored in `object` and `newdata` are different!" using xgboost in mlr package [closed]

I am trying to make a multilabel classification model for XGBoost. I have one that works for RF, but when I try this code below for XGBoost I get the error: ...
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How to tell if a model is overfitting or underfitting or the problem is something entirely different

I'm a complete beginner and I'm trying to do a multi-label classification on the well known dataset ChestX-ray14, which contains about 112 thousand x-ray images from about 31 thousand patients, the ...
Enkhtaivan Ganbat's user avatar
1 vote
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74 views

How to measure agreement with categorical labels, and with multiple binary labels?

I have a dataset in which 7 coders have given a categorical label to each of 152 objects. The same 8 categories are selected between for all objects. I would like to measure the agreement between ...
Ben Jones's user avatar
1 vote
0 answers
329 views

Why Binary Cross Entropy is more suitable than Categorical Cross Entropy in multi label classification?

I found this answers. But, I don't get fully. If I have three labels in multi label classification task, did BCE produce 3 separate outputs? Why we shouldn't use CCE? In this Facebook work they claim ...
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0 answers
82 views

How to avoid overfitting (while training a model and predicting) in a dataset that is basically overfitted?

I need to solve a multi-label classification problem where the dataset itself is the definition of overfitting, in the sense that some labels appear a lot (almost 50% frequency), some rarely appear, ...
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1 vote
0 answers
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Multilabel classification with different number of classes

I have built a CNN with a final dense layer and a sigmoid activation to predict my ground truth. I have 10 variables. Three variables have three levels (classes) and 7 variables have two levels (...
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What are the applications, where different multi-label classification performance evaluation metrics should be used?

There are numerous multi-label classification performance evaluation metrics, namely hamming loss, accuracy (or Jaccard-index), subset accuracy (or exact match), example-based (precision, recall, f1-...
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