Questions tagged [multi-label-classification]
For questions related to the multi-label classification problem, i.e. each example (or instance) can be labelled with more than one label.
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Approaches for multi-label classification with over 1,000,000 labels
I have billions of rows in some dataset and each row can be in any subset of about 1 million binary labels. So the number of overall classes would be $\sim 2^{1,000,000}$, if I were to think about it ...
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Backpropagation with multiple output neurons but only one loss value
Suppose we have the following neural network (in reality it is a CNN with 60k parameters):
This image, as well as the terminology used here, is borrowed from Matt Mazur
As is visible, there are two ...
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Multilabel text classification with highly imbalanced training data
I'm trying to train a multilabel text classification model using BERT. Each piece of text can belong to 0 or more of a total of 485 classes. My model consists of a dropout layer and a linear layer ...
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Is it a good idea to have a category and its subcategories in the training set of an object segmentation model?
I am currently training an object segmentation model (detectron2 : mask rcnn)
The objective is to detect materials like wood, plastic, glass etc...
wood is one of the categories in my training set.
Is ...
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Multi label classifier for patch wise predictions
If I train a multi label classifier on full images and then I feed some patches of these images will it accurately generate the labels which comes in that patch?
For example if I train an image ...
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Image classification problem with multiple right classes
I have a use case where the model needs to detect fabricdefects. There are 15+ different kinds of defects. In one image there can be multiple defects present. The straight forward solution for this ...
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How to define a loss function for multi-label problem?
I have voice recordings which are labelled by not only a single label but multiple labels. Each voice recording corresponds to one of class labels within a set. In other words, the training instance ...
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Convolutional network for multilabel classification in NLP
I am trying to label code snippets and I base on this article: https://arxiv.org/pdf/1906.01032.pdf
My dataset is just code snippets (tokenized as ascii characters) and 500 different labels from ...
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What is the difference between multi-label and multi-task classification?
I am working on a data-set that has multiple labels associated with it (not necessarily independent of each other). During my development, I am confused if I should consider it as a multi-class ...
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What's the best way to train data with unbalanced targets?
Suppose I have data I want to use for supervised learning, but there is a pretty bad target/class/labels imbalance. Should I:
Limit the size of the training set to make sure there is a flat target/...
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What could cause the hamming loss and subset accuracy to get stuck in a multi-label image classification problem?
I am rather new to deep learning and got some questions on performing a multi-label image classification task with keras convolutional neural networks. Those are mainly referring to evaluating keras ...
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Do the terms multi-task and multi-output refer to the same thing in the context of deep learning?
Do the terms multi-task and multi-output refer to the same thing in the context of deep learning (with neural networks)? For example, do neural networks for multi-task learning use multiple outputs?
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Is it valid to implement hyper-parameter tuning and THEN cross-validation?
I have a multi-label classification task I am solving. I have done hyperparameter tuning (with Keras Tuner) to determine the best configuration for my neural network.
Is it valid to do this (determine ...
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What are pros and cons of using a multi-head neural network versus a single neural network for multi-label classification?
I haven't been able to find a good discussion specifically comparing the two (only one describing a classification and regression problem). I am training a classifier to learn both age and gender ...
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Why is the validation loss less than the training loss, and what can be said about the effect of the learning rate?
I have the following results I am trying to make sense of. I have attached the loss curves here for reference.
As you can see, the first issue is that the validation loss is lower than the training ...
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Multi-target regression using scikit-learn without ytrain
I would like to use the multi-target regression with scikit-learn. However, the examples I've seen use Xtrain and ytrain?
What is ytrain in regression?
I know y it is used for classes in ...
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What does "adding class weights for an imbalanced dataset" mean in the case of multi-label classification?
Suppose I have the following toy data set:
Each instance has multiple labels at a time.
You can see I have 2 instances for Label2. However, only one instance for the other labels. It means that we ...
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CNN to detect presence/absense of label on images with mixed labels
Here's my problem: I work with medical image classification, and currently I have 3 classes:
class A: images with lesion 1 only; and images with lesion 1 and N other lesions
class B: images with 2 ...
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How do you measure multi-label classification accuracy?
Multi-label assignment is the task in machine learning to assign to each input value a set of categories from a fixed vocabulary where the categories need not be statistically independent, so ...
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how to handle highly imbalanced multilabel classification?
I am working on a multilabel classification in which I am having 206 labels. When I saw the percentage of the number of 1's in each label they are way less than 0.1% for each label. The maximum ...
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Should I use additional empty category in some categorical problems?
I try to create autonomous car using keyboard data so this is a multi class classification problem. I have keys W,A,S and D. So I have four categories. My model should decide what key should be ...
user40943
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How do RNN's for sentiment classification deal with different sentence lengths?
I have been doing a course which teaches you about Deep Neural Networks, during one of the exercises I was made to make an RNN for sentiment classification which I did, but I did not understand how an ...
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Can I do image classification with Multi Layers Perceptron (MLP)?
I'm seeking guidence here.
Can I use Multi Layers Perceptron (MLP), e.g regular flat neural networks, for image classification?
Will they perform better than Fisher Faces?
Is it difficult to do ...
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How to train a LSTM with multidimensional data
I am trying to train a LSTM, but I have some problems regarding the data representation and feeding it into the model.
My data is a numpy array of three dimensions: One sample consist of a 2D matrix ...
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Neural network to extract correlated columns
I want to use a neural network to find correlated columns in a .csv file and give them as a output. The input .csv file has ...
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Recent algorithms for correcting mislabeled data using multilayer perceptrons
I am doing literature research on algorithms for correcting mislabeled data using multilayer perceptrons. Found an "old" paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al....
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Are the labels updated during training in the algorithm presented in "An algorithm for correcting mislabeled data"?
I am trying to understand an algorithm for correcting mislabeled data in the paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al. The authors are suggesting to update the ...
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Why is there more than one way of calculating the accuracy?
Some sources consider the true negatives (TN) when computing the accuracy, while some don't.
Source 1:
https://medium.com/greyatom/performance-metrics-for-classification-problems-in-machine-learning-...
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Is this neural network with a softmax in the output layer suitable for multi-label classification?
I have data with about 100 numerical features and a multi-labelling that encodes ownership of a certain product (i.e. my labels are of the form $[x_i, i=1, \dots, n]$, where $n$ is the number of ...
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Which other loss functions for hierarchical multi-label classification could I use?
I am looking to try different loss functions for a hierarchical multi-label classification problem. So far, I have been training different models or submodels like multilayer perceptron (MLP) branch ...
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