Questions tagged [multilabel]

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

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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 ...
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Hybrid approach for text categorization (Rule based + ML)

I want to build a multi-label text categorization system of paper abstracts with about 20 categories. For many of the categories keyword based logical rules exist with a low false positive and medium ...
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why my neural network predicts (badly )only one class in multilabel classification? [duplicate]

I am preparing an academic project but I am stuck. Hoping in your help my problem is a multilabel classification problem on 7 classes Before was 8 classes but the last class was very complex so I ...
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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 ...
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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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Learning multi-label classification from single-label samples?

I am trying to design an online training regime for a neural network where have samples that can be any number of $k$ classes, but I only have access to samples with single labels. For example, if $k=...
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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. ...
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How to fix label errors in text corpus?

I have a huge text corpus (around 60k+ documents with 40 classes. But the corpus suffers from class imbalance problem. Also, the ...
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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 ...
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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. ...
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connection between multi-label classification and multi-class classification

For a dataset with multi-label judgement, e.g., coco dataset but where we only want to predict the most-possible label. There're multiple ways, for example : 1) train as a multi-label learning(each ...
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Training a multi label classification where each example should only create an error for certain labels during training

Imagine the following problem: You want to predict how likely it is that a person (with a set of features that you can train on) who visits a certain country will also go and see the capital city. So ...
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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 ...
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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 ...
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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 ...
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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 ...
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Is my task Multilabel-multiclass or multitask classification

I have to find a suitable model for recognizing different events within video clips. I am a little bit confused in considering my task as a multilabel-multiclass classification or multitask ...
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Machine Learning with multiple correct labels

In my problem setting of a regression problem, the label of an instance is not only real valued vector but a set of real valued vectors. Picking one specific vector from the set as the correct label ...
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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 ...
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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 ...
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Using Hamming Loss and Exact Match Ratio on Keras for multilabel classification

I am working on a multi-label classification problem and it requires hamming loss and exact match ratio to be used for evaluation with test.csv after training with train.csv. It seems that keras does ...
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Is this scenario a multi-task learning or a multi-label classification?

I would like to predict the Vehicle Descriptor Section (VDS) of Vehicle Identification Number (VIN) based on features like vehicle year, make, model, engine size, body type, etc. Expected output: A 5 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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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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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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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Can the label powerset method be used to predict probability for reach class?

The label powerset is a method used to transform a multi-label problem to multi-class problem. The idea is straightfoward, just enumerate all the possible combinations of outputs, and treat each of ...
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Training samples with no labels: To include or not to include?

I am working on a multi-label classification problem. Each sample is capable of taking more than a single label. Sometimes samples don't have any labels associated with them. My dataset has 50% ...
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HammingLoss vs. Log-loss evaluation metrics for multi-label classification problems

I am trying to understand the trade-offs between different metrics for evaluating the performance of different classification methods for multi-label data. One option commonly found in the literature ...
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Is there a label smoothing version for multi-label classification?

I use label-smoothing for multi-class single label classification as follows. ...
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Metrics for Multilabel Classification

From what I've read, F1-score is a commonly used metric to assess the performance of a multilabel classification problem. However, I recently came across mAP@K and mAR@K as metrics used for ...
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Multilabel Tweet Classification

I need some general advice and possible ideas. Problem statement goes like this -- We are given a tweet and we have to specify associated labels for it like generalized hate, support, oppose, ...
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Multi-class classification with prior knowledge of class similarity?

Backrounds I would like to build a model that predicts a month label $\mathbf{y}$ from a given set of features $\mathbf{X}$. Data structure is as follows. $\mathbf{X} : N_{samples} \times N_{features}...
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How to approach a multilabel classification problem where the proportions of the predicted labels matter?

My original task was to classify various cell types (the classes) based on gene expression patterns and this problem simply involves predicting one label from multiple classes. This was done easily ...
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How to get boundary points that are at interface of differnet classes in multilabel classification dataset

I am working on finding points which are at boundary of different classes. In other words finding points on which a classifier would be most confused or uncertain about. For a setting like multi label ...
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Ranking most probable labels from multilabel classifier

I have been working on a multilabel classification problem. I want to classify whether each of 25 labels is present on a given sample. The labels are not mutually exclusive. Ultimately, I would like ...
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Multilabel classification without using sklearn.multioutput.MultiOutputClassifier

Could someone recommend a tutorial that shows how to solve the multilabel classification problem, by using one-vs-rest approach, but not using sklearn.multioutput.MultiOutputClassifier. The ...
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1 answer
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Using pos_weight to improve recall in a multi-class multi-label problem

I have a multi-label classification problem, and so I’ve been using the Pytorch's BCEWithLogitsLoss. I’d like to optimize my model for a higher F2 score, and so want to bias it to have greater recall (...
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Multi-label Text class

The data i am dealing with are simple text sentences that needs to be classified into variaous labels that correspond to the different topics as simple as Yes/No class. Several labels can be assigned ...
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What is the difference between multi-class and multi-label classification? [duplicate]

What is the exact difference between multi-class and multi-label classification? For example, if you have a fridge with a camera that can view inside to see what products are still in stock, is this ...
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categorical distribution in validation set

I have a dataset that contains 6877 samples. This is a multiclass multilabel classification which means that we have 9 classes and every sample can belong to one or more of these classes. The total ...
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Can target encoding be performed on a multi-label classification problem?

Is there a way to perform target encoding on multi label (closed set) problems, obviously target encoding is used on multi-class problems all the time, but i'm wondering if it works for multi label ...
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Initial loss for a multi-label prediction problem

I have seen this: https://datascience.stackexchange.com/questions/18991/is-there-a-rule-of-thumb-for-the-initial-value-of-loss-function-in-a-cnn Since my question is more mathematical in nature, I ...
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