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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Generic object detection - unspecified list of classes and high accuracy

As a part of a small project, I would like to create tags for a set of pictures (posters). I know that if I want to recognize a lot of objects I need to have a model which was trained on a large ...
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24 views

What is the formula to calculate the precision, recall, f-measure with macro, micro, none for multi-label classification in sklearn metrics?

I am working in the problem of multi-label classification tasks. But I would not able to understand the formula for calculating the precision, recall, and f-measure with macro, micro, and none. ...
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27 views

Multiclassification Error: NotFittedError: This MultiLabelBinarizer instance is not fitted yet

After picking the model, when I try to use it, I am getting error - "NotFittedError: This MultiLabelBinarizer instance is not fitted yet. Call 'fit' with appropriate arguments before using this ...
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16 views

Making sense of a accuracy plot for a 5 fold training using random forest

I'm using sklearn.model_selection.learning_curve for 5 fold training of data. The code is as given below. ...
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23 views

Multi label text classification from thousands of labels

I don't have machine learning experties, but I'm working on a project that has text classification requirements in it. The easiest approach I was able to understand was using fasttext.. It worked, but ...
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40 views

Decision Tree Classifier to classify values based on values of other columns

I have data with multiple labels, for example My X set is fromt second to third column, and I want to classify either first column or the last column, so I made my Y the last column. The goal is so ...
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unique predictions for “multi-label multi-output” classification task

Let’s assume that four participants (A, B, C and D) take on five sport-challenges (e.g. swimming, running, ...). Our goal is to predict the placement of each participant for each challenge. Moreover, ...
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36 views

Multi-label classification based on single-label dataset

I'm looking for a solution to detect different moods/styles expressed by an image. Unfortunately, there is no multi-labeled dataset for this task. The scenario of defining a multi-label ...
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One label dominates in a multiclass classification problem when mapping scores to labels

I am supposed to map each person in my dataset to one of the n categories based on his propensity score. To achieve that, I constructed n models and obtained scores for each category. I did ...
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22 views

Can i build an image classification model where each image has multiple labels?

If I am building a model where I need to predict the vehicle, color of it, and make of it, then can I use all the labels for a single image and build my model around it. Like for a single image of a ...
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Why does classifier chain ask for at least 2 classes, when I have it

I'm using Classifier Chain with logistic regression and when i try to use fit, i get This solver needs samples of at least 2 classes in the data, but the data contains only one class: 1 but I'm ...
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Trained CNN individually on multiple images to classify them, how can I now classify a related “set” of these images that correspond to one object?

I have a N object classification examples, each example consisting of a set M individual images of the object at different angles. I've trained M CNNs with the dataset of one particular image angle ...
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Metrics for multi-class classification. When the prediction is of low quality

I am having a multi-class classification problem, so prediction of an instance to which class belongs to. I am reading that the typical metrics like accuracy or score are very "strict" on such ...
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Classification accuracy based on top 3 most likely classifications

My goal is to recommend jobs to job seekers based on their skill set. Currently I'm using an SVM for this, which is outputting one prediction, e.g. "software engineer at Microsoft". However, consider ...
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How to best use Large images in training set for deep learning

I would like to ask you about how I should deal with the images I have. They are really large. They have this shape: (3000, 4000, 3). I'm working on a multilabel classification model. And I want to ...
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39 views

Classification accuracy of a Random Multi-label Classifier

What is the exact accuracy of a random classifier which has n labels (say 1000) where k labels (say 50) are true? Can I say the accuracy of a random classifier has an upper bound of k/n? -Edit- I ...
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Random crop in multi-label image classification context

In some research papers, people use random cropping with variable sizes and then they resize them to original size as a data augmentation technique saying that it helps boost results. Can someone help ...
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How to select 500 most pertinents tags among 10000?

Say we have 100,000 documents tagged with 10,000 different tags (Max 5 tag per document). We wish to limit allowed tags to a list of 500 tags. How to select 500 tags in order to cover the largest ...
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34 views

How to feed data to multi-output Keras model from a single TFRecords file

I know how to feed data to a multi-output Keras model using numpy arrays for the training data. However, I have all my data in a single TFRecords file comprising several feature columns: an image, ...
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128 views

XGBoost for multi-label image classification

I am trying to use the xgboost classifier for a multi-label and multi-class image classification task. I have a list of images that can have up to 5 different labels in each of them. Before I use the ...
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53 views

One hot encoding for multiple label(trainy) in .fit() method?

I have a mobile price classification dataset in which I have 20 features and one target variable called price_range. I need to classify mobile prices as low, medium, high, very high. I have applied a ...
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Looking for recommendations on auto-tagging approaches

Basically looking for approaches to solve the stackoverflow question tagging problem. I have seen a few papers already but in case I have missed something - asking here too. For anyone interested, ...
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Multi-label classification with missing labels

I have a neural network that generates a vector that represents the class probabilities. Since it is a multilabel classification problem, I'm supposed to train the network using sigmoid + binary cross-...
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211 views

Using LSTM for multi label classification

I am trying to use LSTMs to train and predict authors using reviews data and metadata ...
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1answer
16 views

Same probability for all classes

I implemented a fully connected MLP of shape [783 (input), 128 (hidden layer) and 10 (output)] the hidden layer had a sigmoid activation function and the output a sofmax. I tested with the dataset of ...
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19 views

Any previous work to develop time series models for multiple label sets with different sampling rates

We have time series data with multiple labels: one label is sampled every day and the other label is added every one month and we would like to train the model accounting for both labels at the same ...
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Improve the results of imbalanced multi-classification multi-lables data

I have 10k rows of multi-classification (x1..x27,y), size of dataframe is: 28*10k and its ...
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56 views

how can I replicate working of Multi Label Binarizer from sklearn package in R?

I want to achieve same working of MultiLabelBinarizer from sklearn.preprocessing package in R. I have list of labels for each example (for Predicated and Actual) like below. ...
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18 views

Neural Network to classify target subitens?

Nowadays i am doing a research project where i am allowed to classify given a sample from a large dataset with an already existed sample/target model the belonging target, but in my project there are ...
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1answer
53 views

Risk score from Neural Network classifier (more than 2 categories)

I am trying to use a Neural Network to perform multiclass classification. The classes represent Insurance Risk Level. The most risky level is Level 1, the least risk corresponds to Level 10. The ...
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Scaling ML/DL classifier

I have been trying to find some guideline through google/stackoverflow for scaling a classification system. E.g. how can I scale a face recognition system if we want to add new people into the system? ...
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31 views

Multilabel image classification failure with a specific dataset

I'm having an issue with a specific dataset. My training for multilabel image classification is returning [class1 and or class2 and or class 3] (only 3 classes for every image) when there are 13 ...
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55 views

Weighing each label in multi-label classification

If, in addition to predicting labels using a multi-label classifier, I'm interested in predicting the weight of each label, what approach should be taken? To give an example, let's say I'm trying to ...
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25 views

Issue with multilabel image classification only returning a couple (incorrect) classes

I followed this tutorial: https://medium.com/@vijayabhaskar96/multi-label-image-classification-tutorial-with-keras-imagedatagenerator-cd541f8eaf24 and wrote some of my code for multilabel ...
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Cross_validation is decreasing accuracy?

I have certain dataset to train a model. The dataset is not very small in size. First, I split the dataset into training and validation data using traintestsplit (80-20), train the model on training ...
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63 views

Split tuples with labeled samples in training, validation and test sets

I was reading through all the internet and i can't find nothing similar what i am looking for, i only saw this topic for pd.DataFrame, np.ndarray and list datasets but i didn't find nothing explaining ...
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32 views

Classify the input set into categories based on pre-defined rule set

I'm trying to solve a problem where there are 2 input files given, Input 1: A set of strings in the format. All the strings start with "A". A-B-C-D A-B-C-3 A-X-Y-Z-4 A-X-5 A-X-P-Q-R A-X-P-Q-S A-M-9-...
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56 views

Improving classifcation when some are less represented?

I have a multi-class classification problem. It performs quite well but on the least represented classes it doesn't. Indeed, here is the distribution : And here are the classification results (I took ...
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Big dataset for multi-class classification can't be dasked and split, normal one can't be handled

I have a huge dataframe (550MB), the lending club one available here, and I have to predict the class of the grades. The dask dataframe is : ...
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28 views

What (probabilistic models) can only output decisions when they are certain?

I'm basically looking for approaches, models, algorithms for the following situation (a fault diagnosis problem): I have an input set $\{x_i\}_{i \in \{1..m\}}$ with $n$ binary features of cases (...
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1answer
33 views

Data quality improvement as a part of preprocessing: Imputation

I have a python pandas dataframe representing a superset. The data contains a lot of nulls which I want to overwrite with real values. the superset has: both numerical and categorical data some ...
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170 views

Using a LinearSVC() for multilabel classification with MultiOutputClassifier() in a pipeline in sci-kit learn

My input data is a (23948,) pandas.Series of strings containing newspaper headlines. My target are 20 labels of the headline (e.g. 'crime', 'politics') each binarily encoded with [0, 1]. The labels ...
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14 views

Multilabel Image classification via multiple binary datasets

I want to make a multilabel image classification model that can detect many different labels. For each label, I can get at least 5000 positive examples and 5000 negative examples. However, my question ...
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1answer
53 views

Best Way to combine multiple datasets into one model

I want to make a multilabel image classification model that can detect many different labels. For each label, I can get at least 5000 positive examples and 5000 negative examples. However, my question ...
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25 views

Multi-label classification + restriction of predicted classes

Good day. Please help to find a solution. I have built classification for goods to choose the best supplier (tf+keras+lstm) using https://www.tensorflow.org/tutorials/keras/basic_text_classification ...
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how use RBF for primal model of svm?

I know if we want to solve primal model of non-linear SVM, we have to generate new features. for example for kernel (1+xz)^2 for primal problem for any pair of features x1 and x2 we have to generate: ...
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solving svm without using largeagian?

I wrote a SVM model in ampl. (multi classification). I am sure the model is right based on SVM. I didn't use lagragian just solved linear svm . But the result are not make sense to me . most of ...
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can we have different features for different hyperplanes in SVM?

is it possible if we have different features for different classes of svm? For example one of the hyperplane: $$w_1\cdot \text{age}+ w_2 \cdot \text{ trip duration} +w_3 \cdot \text{ income}$$ and ...
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147 views

Transform multi-label problem to multi-class problem

What are the downsides of modelling a multi-label problem as a multi-class problem with a single classifier? Let my clarify what I mean. There at least two ways that one multi-label problem can be ...