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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How many training data should I use in multilabel classification?

Now I'm using Keras to implement a multi-label classification model. Specifically, I want to classify who present in an audio clip (maximal 8 people). The label of data has 8-bit, for example, [0,1,0,...
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Error due to Colab RAM depletion when implementing Multi-label classification with BERT and Pytorch

Background: I'm implementing multi-label classification for tones (7 types of tones). Dataset shape: train_df=(5392, 8); val_df = (1348, 8) The modelling approach remains the same as this multi-label ...
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How to apply Helmert Coding in a real Machine Learning model?

My dataset is something like this ...
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train two models separately for multi-label classification

If we have a muli-label classification problem, is that true to train the model over each target separately? For example, if we have a 2-label(y1,y2) classification, once we train a model with y1 and ...
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Merging multiple classfiers

I am designing a classifier that takes as input features matrices of different dimensions, for example (Nx5, Nx10, Nx100, Nx1000) using visual bags of words of distinct dictionary sizes and methods (...
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How to perform Multi-Label Image Classification with EfficientNet

Problem My goal is to perform multi-label image classification with EfficientNet. It should take a picture as input and e.g. tell the user that it sees a person AND a dog on the picture, meaning the ...
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Predicted probabilities of Multi Label Classification

I'm currently working on a Kaggle Competition wich objective is to predict probabilities of an ID belonging to each class. There are 4 posible classes. The data is tabular and because it's a Kaggle '...
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Using a multi-headed neural network, how should I approach the regression head loss

I have a multi-headed NN where one head performs multi-label classification and the other a regression task on a set of images. The classification head outputs a one-hot vector where each value in the ...
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Data augmentation for tabular data in a multi label classification task

The task at hand is to predict the future lab values for a patient (1 if abnormal and 0 if normal) using the previous numerical data. It is a multi-label, multi-class time series classification task. ...
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Best Model for Multi-label image classification [closed]

Usually using CNN architecture with a Sigmoid function as an activation function in the last layer and using binary cross entropy can be used to output a probability for each class. However, the ...
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Class wise opinion for multi-label sentiment classification

I'm trying to build a model which separates positive and negative classes and assigns the label. I have a multi-label review dataset for example: No Review Label 1 Phone is good but charger is not ...
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Evaluating model with categorical target variables

I converted all the numeric target variables of MNIST dataset into categorical variables. So, 0 became zero, and so on. Next, I ...
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How do you perform multilabel classification that is also a multiclass problem?

I have a data set in which each row of data belongs to certain classes/labels. text class1 class2 class3 text1 pos neg na text2 na neg na text3 na neu na text4 pos neg neg text5 neg neg na ...
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Using softmax for multilabel classification (as per Facebook paper)

I came across this paper by some Facebook researchers where they found that using a softmax and CE loss function during training led to improved results over sigmoid + BCE. They do this by changing ...
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Stratification sampling of a json Array [closed]

I have a json array file that i need to create a smaller sample of for testing purposes. A sample of the file looks like: ...
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Not able to get a good accuracy score for the classification problem

I have taken a music popularity dataset which has five classes based on the popularity of the songs.I have made a Random forest model to predict the popularity of a given song(given its features).I ...
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Changing order of LabelEncoder() result

Assume I have a multi-class classification task. The labels are: Class 1 Class 2 Class 3 After LabelEncoder(), the labels are transformed into 0-1-2. My questions are: Do the labels have to start ...
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Feature vector representation

I have a clarification. I have to create a classification model for certain set of documents. We are supposed to flag it anamoly or not based on certain terms in the document. My question is the terms ...
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How to read the predicted label of a Neural Netowork with Cross Entropy Loss? Pytorch

I am using a neural network to predict the quality of the Red Wine dataset, available on UCI machine Learning, using Pytorch, and Cross Entropy Loss as loss function. This is my code: ...
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Best Way to tackle to time series classification problem?

I have a dataset where the input is a dataset for ICU patients where each ICU stay has 40 features (20 vitals, 20 lab values) and multiple time steps (the stays' length is between 6 and 19-time steps)....
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How to train BERT (multi label) on imbalanced dataset for search query category classification

I have a dataset of 2 million search queries relative to 7000 categories. same query could have multiple categories. Aim is to predict category/categories for query with confidence score. I tried ...
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How best to convert label classification into regression?

I have a dataset of genes for which I'm trying to predict genes that cause a disease. Originally I was doing this with a multilabel classification. I had 3 groups: I labeled already known disease-...
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Multi-label text classification. 3-4 labels per text, 100 labels overall

My first pet ML project, so please pardon if I phrase something incorrectly. Recently I had IMDB sentiment analysis binary classification practice on Tensorflow site. Now I am keen to do multiple ...
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Best algorithm/model to establish relevance between events utilizing mixed data type (Tags, Time, x_coordinate, y_coordinate)?

I'm building a relevance ranking system for incidents occurrence and prevention. My goal is to use four attributes to establish relevance: tag (About 500 tags), x_coordinate, y_coordinate and time. ...
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113 views

Test set larger than train set [closed]

There is a two class dataset with 1121 values in total, having 230 from same class and 891 from the other class. The training set is choosen as 230+230=460 from both classes and the test set as the ...
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Suggestions for Open-Source Tool for Image Classifications (with Nesting)

I'm looking for an open source tool to assist my colleagues and I to label images for a machine learning application. We don't actually need bounding boxes or anything to pinpoint regions within each ...
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How to calculate separate binary accuracy metric for each label in a multi-label classification in Keras?

I'm stuck with this: def multilabel_binary_acc(y_true, y_pred): return [K.equal(y_true[:,n], K.round(y_pred[:,n])) for n in range(y_pred.shape[1])] But it ...
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Classifying text as emails or ages

I am trying to classify small strings into three categories. Examples: ...
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1answer
76 views

How to apply MultiOutputClassifier to a dataset for Naive-Bayes algorithm

I have a dataset which is as follows, (it's taken from an article online and I have been trying to Naive Bayesian algorithm on it) Original Dataset y attribute After having done some manipulations (...
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How to cluster label (in a multilabel classification problem) which mostly appear together in a class

To cluster label (in a multilabel classification problem) which mostly appear together in a dataframe? For example, I have this dataframe: ...
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82 views

Multi-label classification with nested features

I need to perform a multi-label classification. I have three features and they are nested. I am unsure how to combine this or what kind of classification algorithm would be best. Some multi level ...
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What kind of learning problem is this?

Say I have $n$ multi-class classification problems $p_1$, ..., $p_n$. Each of these have their own training data. While they are all distinct problems, there may be similarities in their data (which ...
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Softmax regression cost function code [closed]

I really do not understand what does this code do M = sparse.coo_matrix(([1]*n, (Y, range(n))), shape=(k,n)).toarray() The code is related to calculating the ...
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2-label dataset for 3-label classifier?

I have a dataset containing headlines and sentiment related to those headlines. The headlines have been filtered out from another bigger dataset using the following criteria: keep the ones that have a ...
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38 views

Which is better: multi-output model or separate models for similar tasks?

I am working on two problems: classification of images into high-level classes (e.g. shoe, dress, jacket etc.) classification of the attributes of the same images on a lower level (e.g. shoe style, ...
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How to find the feature regions where each label is the most expected when using decision trees?

Given a decision tree for classification for example this one: What is the way to find the feature domain (petal and sepal width and length) where a sample would most likely occur in the feature ...
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ML - Labelling - number of possibilities [closed]

I've started learning ML and stuck with the number of possibilities in labelling. I have a sample which comprises 4 attributes (binary) (from the book Apprentissage artificiel, Antoine Cornéujols) ...
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Which model for a content suggestion in which only some of the items are currently available

I am trying to understand the type of model that would be used in a content suggestion scenario where not all of the choices are available at a given time. For example, when an online movie ...
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407 views

ValueError: bad input shape

I have multilabel problem. I was using onevsrestclassifier and now i want to use onevsoneclassifier. ...
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How many outputs for CNN when dealing with a multi-label classification problem with OneHot Encoded labels?

My labels are of type tensor [1 0 0 1] denoting a 4 label multiclass problem for any given 16x16 image. I'm using BCEWithLogitsLoss from ...
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459 views

MultiLabelBinarizer() with inverse_transform()

I have multilabel labels. Elements in a label mean voting. Here is how labels look: ...
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43 views

How to solve this classification problem: multi-class or multi-label?

In a supervised cancer classification task which is given the data containing metrics we want to classify whether the patient has cancer or is at high risk (label 1) or low risk (0). However, there is ...
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Multilabel Classification - Overfitting?

My task is the following: To input drug combinations and output renal failure-related symptoms from the drug combinations. Both the drug combinations and renal-failure related symptoms are represented ...
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2answers
122 views

How can I label (predict) an unseen set of data based on an existing model?

I'm working on a learning multi-label classification project, for which I've taken 16K lines of text and kind of manually classified them achieving around 94% of accuracy/recall (out of three models). ...
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1answer
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Improving misclassification for one class in a multi-class classification task

Here I am trying to use 3 convolution layer neural network to classify a set of images (train data: (3249) , validation data: (487), test data: (326)) I have one class which is misclassified and I ...
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Is it better to have one model with more categories or less with two for multi-label classification?

For classifying text into three classes question, complain and complements where each sample can have multi-labels (question and complains, question and complements): is it better to have one model ...
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Positive/negative training sample imbalance in multi-label image classifiers

I'm trying to train VGG-16 on the Pascal VOC 2012 dataset, which has images with 20 labels (and a given image can have multiple classes present). The examples are highly imbalanced, so I've "...
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Is there a deep learning method for 3D labels?

As the question says, I want to feed labels into a neural net that are three dimensional. Let's say that I have 3 possible labels and each one of my data points corresponds to a percentage of those ...
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Why Overfitting sometimes appears when compile model multiple time, is it normal?

At the time I got small datasets of brainwaves (EEG) (105 samples) for 3-class classification problem. I split my data into 3 part: Train data = 90 (data) Validation data = 10 (data) Test data = 5 (...

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