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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17 views

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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14 views

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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42 views

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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37 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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25 views

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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16 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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44 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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3answers
50 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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50 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
15 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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1answer
44 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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17 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
52 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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26 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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1answer
50 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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22 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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7 views

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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39 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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2answers
54 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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47 views

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
28 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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1answer
105 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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13 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
29 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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24 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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14 views

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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15 views

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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120 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 ...
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28 views

how to reduce the load time of tensorflow text classification model.?

I want to reduce the load time of the model, when testing the model to predict the categories using tensorflow text classification. I am getting the raw data from mongodb collection, which has over 1 ...
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23 views

Can we have a sampled sigmoid instead of softmax?

Thh solution proposed here: is for softmax negative sampling. How do we do a sigmoid negative sampling? I couldnt find a corresponding 'tf.nn.sampled_sigmoid_loss' function.
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18 views

Does validation loss increase if the dataset is small?

This is my loss vs epoch image... You can see that my model converges too early. However, the frustating point is validation loss does not decrease accordingly compared to training loss. I am doing ...
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1answer
36 views

Is there an algorithm for categorizing unlabeled samples into K classes? [closed]

I am not sure if this would be considered unsupervised, or semi-supervised learning. I am looking for an algorithm that will take N input arrays of features, and then cluster samples(not features) ...
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1answer
44 views

Which classification algorithms are negatively affected by class imbalances?

I've seen a few posts and papers floating around the web (mostly those related to over/undersampling, SMOTE, and cost-sensitive training) that, when discussing class imbalance, specify that certain ...
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25 views

Is Elmo equivalent to Fasttext+Bi-directional GRU?

From what I have read, Elmo uses bi-directional LSTM layers to give contextual embeddings for words in a sentence. So if I use a bi-directional LSTM/GRU layer over Fasttext representations of words, ...
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62 views

Regarding multi label classification

I am performing multi label classification in python using sklearn. Here is the classification report ...
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1answer
19 views

Predicting labels which are not independent of one another

I want to make a multilabel classifier where the labels are dependent on one another. Concretely, I have a situation where: If label A is true, then label B is also true. If label B is true, label A ...
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146 views

Regarding imbalanced multilabel dataset

I am doing multilabel news classification in python language.The dataset I have has two files. First CSV contains articles at each row. Second CSV contains corresponding labels to each article. Here ...
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1answer
68 views

what is the difference between multilabel and multilabel-multiclass classification?

I am trying to classify news articles into their required category. However I am confused by the above(multilabel and multilabel-multiclass) terminologies. My dataset consists of 2 csv files. The ...
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85 views

Algorithms inherently supporting multilabel classification

In the documentation of sklearn, it says that several algorithms inherentrly support multilabel classification, such as RandomForest or MLP : https://scikit-learn.org/stable/modules/multiclass.html ...
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61 views

How to tackle a multilabel classification problem

I am trying to build a LSTM model for a multiclass classification problem on textual data. Until now, I have only built a model when one input belongs to one of the categories. What do I do when one ...