Questions tagged [multilabel-classification]
Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.
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Multiple classes present in one-hot encoding
When dealing with classification for multiple classes present in the same sample, can the output layer have the form of one-hot encoding, but instead of only one hot, have multiple?
That is, in case ...
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Movement in cohorts
I am working on a user sales data which gets updated week over week. Based on the sales done in each week, the user is categorized in segment A, B or C. This means size of each segment could change ...
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Multi-label classification inference
I am working on a multi-label classification with transformers. I want to assign tags to input text. First, I have trained a model multiclass and with the pipeline function I can retrieve all possible ...
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How to approach a problem of identifying misclassified categories
So I have a dataset of where people have to classify repair cases narratives according to a dropdown. The dropdown has a default type and my boss informs me that an unknown number are probably ...
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multi label classifictaion - single class or group labels?
I currently working on a vehicle dataset, with the goal of detection and classification vehicle types (car, bus, truck, motorcycle).
In addition, for each vehicle, I want to detect and classify each ...
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Large change in validation loss, small change in training loss
I'm training a multi-task, multi-label neural network. I am attempting to tune the architecture and am having some trouble interpreting the learning curves. Particularly, when I look at the learning ...
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What statistical model suits for this problem?
I have a dataset with 6 target variables and the target variables are Boolean. The requirement is to use logistic regression to build the model. Which ML approach can be used in this situation?
Will ...
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Mulitlabel stratified k-fold splitting with non-overlapping groups
For multilabel stratification, we have a good solution implemented by scikit-multilearn which I believe is based on the algorithm presented in "On the Stratification of Multi-label Data". ...
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Supervised UMAP on multi-label data
Is training a semi or supervised dimensionality reduced space with UMAP using multi-label targets supported & known to yield meaningful results (with respect to the unsupervised embedding)?
The ...
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How to include information about labels in a multilabel classification task
Currently, I'm working on a multilabel classification problem for a shared task in NLP. I have quite a few labels, and with those labels, I have a little paragraph defining them. I was wondering if ...
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IndexError: index 50 is out of bounds for axis 1 with size 6
I'm using WHU-OPT-SAR Dataset having 7 classes describes as follow:
And using this code. I changed it according to the dataset still I'm getting the error
I think it is because of the label number ...
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Can I transform multi-class data that has 3 classes to different number of classes?
I've seen cases where the author reduces the number of labels/classes -- e.g. classification of 5-star based reviews to 3 classes (positive, negative, neutral) by changing the label of 4 and 5 stars ...
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Best approach for rule-based system in multilabel classification-problem?
I’m new to the world of NLP and am looking for some guidance. I want to create a rule-based system that “grades” text in accordance to some set of criteria. For example, one criteria could be “The ...
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Which model to use for multitarget classification with strong class imbalance and many categorical variables?
I have a small dataset 79 observations in 21 variables.
Almost all the variables are categorical variables in the format yes/no or 1/2/3.
I would like to predict jointly three of these variables ...
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How do I Classify text data with multiple sub labels? [closed]
If I have data that was like this
How can I train keywords and try to classify them into these labels what was the best algorithm to try this and labels are not limited to 4 might increase and some ...
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The paradox of Imbalanced binary classification ¿To do something or to do nothing?
Context: Suppose we are interested in deploy a machine learning model to solve a problem of binary classification; furthermore, assume that the dataset $\mathcal{D}$ for the training of our model ...
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We pose recommendation as extreme multiclass classification problem, what is a class here? is it video category? or the video itself?
In the Youtube video recommendation paper, the author talks about candidate generation is a multi class classification problem, I am trying to understand what the classes here, a video category or the ...
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What is the effect Cross-Multi-Labeling/Annotation on learning process?
I have a philosophical question regarding training convolution neuronal network. I am work on training NN for purpose of detection of Window and Window blind. This is an issue of cross labels; that is,...
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Beginner Question on Understanding Linear Classifier
I have been trying to understand the math behind Linear classifier for images and I'm hitting a roadblock to understanding this image below:
I can to some extent agree that we stretch the pixels into ...
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Label network embedding as input features for multi-label classification
Leveraging correlation between labels is an essential aspect of multi-label classification. I am trying to figure out the best approach for incorporating label correlation information for my task. One ...
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Hierarchical Dependent Multilabel Multiclass classification
I am aware of both multi-class and multi-label classification.
But here is my use case,
X1,X2,....Xn ==> Level1,Level2,Level3,Level4,Level5.. Image attached of a contrived example.
So the model's ...
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Are more target labels in a multi-label classification always better?
Context
We work on medical image segmentation. There are a lot of potential labels for one and the same region we segment. There can be different medically defined labels like anatomical regions, more ...
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Understanding SGD for Binary Cross-Entropy loss
I'm trying to describe mathematically how stochastic gradient descent could be used to minimize the binary cross entropy loss.
The typical description of SGD is that I can find online is: $\theta = \...
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Text similarity for badly written text
Consider the following scenario:
Suppose two lists of words $L_{1}$ and $L_{2}$ are given. $L_{1}$ contains just bad-written phrases (like 'age' instead of '4ge' or 'blwe' instead of 'blue' etc.). On ...
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Multi-Label time-series classification with LSTM: large performance decrease for longer periods
I have daily data on event occurences, so for each day I have a vector like [1, 0, 1] indicating that on this day event one and three occured, but event two did not occur. I want to train a model to ...
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best way to convert labels
I need to convert label Win,Lose,Draw to continous number,
thich is the best method to do it? Win=1 , Draw=2, Lose=0 ?
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Why is Word2vec regarded as a neural embedding?
In the skip-gram model, the probability that a word $w$ is part of the set of context words $\{w_o^{(i)}\}$ $(i= 1:m)$ where $m$ is the context window around the central word, is given by:
$$p(w_o | ...
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Custom multi-label cross-entropy loss that boosts weight of particular errors
I am using XGBoost for a multi-label classification problem (objective is 'multi:softmax' in XGBoost). In my case there are 16 discrete output labels where only one is correct. However, depending on ...
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Text to Text classification
I am new comer to the field of data science and have been struggling with a simple classification problem. It seems to be generic enough and I have a suspicion that there must be a better way to frame/...
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Methods for combining instance observations for classification
I am working on a project where I classify tiny moving particles into a few classes (fibers, hairs, glass shards, bubbles). The particles are only a few pixels large and are observed in a few frames ...
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Classification metrics can't handle a mix of multilabel-indicator and multiclass targets
I am having a problem due to encoding of multiple categorical variables and building a multiclass classification model.
I have extensively read and searched for a solution but I am clearly missing ...
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Method for multi-label category classification
I’m working on a project that involves a Natural Language Processing methodology. I want to classify categories(label) to biomedical news articles (it can be multi-label)
(For example, News 1: ...
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Can we recognize different events in time-series data by patterns?
I'm currently have to deal with multiple time-series datasets with the same type of patterns. My quest is to find a way to label these data points (or may be intervals) correctly. Below is how the ...
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Multi-label Classifier underperforms individual classifiers
I originally trained multiple individual binary classifiers for each label of an image. Then, I realized I can train a single multilabel model for this task. I used binary_cross_entropy loss for this ...
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Conventional way of representing uncertainty
I am calculating metrics such as F1 score, Recall, Precision and Accuracy in multilabel classification setting. With random initiliazed weights the softmax output (i.e. prediction) might look like ...
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Class imbalance: Will transforming multi-label (aka multi-task) to multi-class problem help?
I noticed this and this questions, but my problem is more about class imbalance. So now I have, say, 1000 targets and some input samples (with some feature vectors). Each input sample can have label ...
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Computing precision in case of multi-label classification
When evaluating a multi-label model for precision by averaging the precision of each sample, would it be appropriate to a) ignore those samples where no prediction is being made? Or is it more ...
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Predict indices of text using deep learning
I want to predict the start and end indices of text where a certain type of propaganda technique is used like smears, name-calling, loaded language etc. Some examples from the dataset are:
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Why do we need "MultiOutputClassifier" if we can get same results without it?
I am learning about multi-label multi-classification examples
It is when you have a case like this
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How to train multioutput classification with hyperparameter tuning in sklearn?
I am working on a simple multioutput classification problem and noticed this error showing up whenever running the below code:
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How to show combined overall accuracy for a multi-ouput model in Keras?
I have a model of the following structure. It has 6 outputs. Given an image, the model predicts classes of 6 different components from the image.
The metrics I used are:
As you can see it outputs an ...
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Multi-label one-hot encoding
So im having this paticular problem triying to do one hot encoding on multilabel data, the encoder is reading more classes than it should, and i dont know why.
let me show you:
Here's my data (17 ...
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Transform multi-class problem to multi-label problem
I found this question but I need an answer to the other direction.
Example:
Let's say we want to predict if a person with a certain profile wants to buy product A and/or B. So we have 2 binary classes ...
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How to multi label text Classification using Deep learning
I am new to the multi-label text classification using Deep learning,
I have Data like this:
...
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Some questions about supervised learning, model evaluation and preprocessing [closed]
I've been trying to employ some basic techniques of supervised learning on a dataset that I have and I have several questions about the overall procedure (i.e. data preprocessing, model evaluation etc)...
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Error when trying to predict BERT model and obtaining classification report [closed]
I am following this tutorial about multi-label, multi-class classification using BERT. I am trying to get the predicted classification for y_pred, but when running ...
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Do I need a multilabel classification machine learning methodology or is it unnecessary?
Introduction
I’m working on a social science research project that involves a Natural Language Processing methodology. I’m assigning multiple labels (For example, label 1: ...
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How to handle unclassifiable data in the dataset
Premise:
Classification problem
Input is three text fields
Output classes are A, B, A&B (Note: A and B are not always exclusive though usually are, hence the 'A&B' class)
Sci-Kit Learn is the ...
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Two-level (large category and small category) label classification problem
At present, there is an app classification task, the input is the function description of the app, and the two labels are the major category to which the app belongs and the small categories under the ...
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