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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47
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3answers
136k views

Understanding predict_proba from MultiOutputClassifier

I'm following this example on the scikit-learn website to perform a multioutput classification with a Random Forest model. ...
19
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1answer
4k views

What does it mean to "share parameters between features and classes"

When reading this paper there is a line which says "linear classifiers do not share parameters among features and classes." What is the meaning of this statement? Does it mean that linear ...
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2answers
6k views

Deep Learning with Spectrograms for sound recognition

I was looking into the possibility to classify sound (for example sounds of animals) using spectrograms. The idea is to use a deep convolutional neural networks to recognize segments in the ...
9
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7answers
15k views

Python library that can compute the confusion matrix for multi-label classification

I'm looking for a Python library that can compute the confusion matrix for multi-label classification. FYI: scikit-learn doesn't support multi-label for confusion matrix) What is the difference ...
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3answers
17k views

How to use sklearn train_test_split to stratify data for multi-label classification?

I am attempting to mirror a machine learning program by Ahmed Besbes, but scaled up for multi-label classification. It seems that any attempt to stratify the data returns the following error: ...
8
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3answers
7k views

Where can I find freely available multi-label datasets online? [closed]

I'm trying to find multi-label classfication datasets, which are available for free online. By "multi-label" I mean that each instance can be labeled with anywhere from a single to $k$ labels, where ...
8
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2answers
86 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 ...
8
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1answer
3k views

Naive Bayes for Multi label text classification

How to use Naive Bayes for multi-label text classification in R. I tried using naiveBayes() from e1071 library but it seems that while training, it doesn't accept multi-label class variable. I ...
7
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1answer
8k views

Dealing with extreme values in softmax cross entropy?

I am dealing with numerical overflows and underflows with softmax and cross entropy function for multi-class classification using neural networks. Given logits, we can subtract the maximum logit for ...
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2answers
5k views

AUC-ROC for Multi-Label Classification

Hey guys I'm currently reading about AUC-ROC and I have understood the binary case and I think that I understand the multi-classification case. Now I'm a bit confused on how to generalize it to the ...
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4answers
9k views

Multi target classification for different types of target variables

I am new to machine learning and I got this task in my university. I have a dataset with over 100 columns and two target variables: $target1$ is categorical i.e. $0$ or $1$ and $target2$ is continuous ...
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3answers
4k views

How mean and deviation come out with MNIST dataset?

I am a novice at the data science, and I notice some repository state the mean value and deviation in MNIST dataset are ...
5
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2answers
1k views

Multilabel image classification: is it necessary to have traning data for each combination of labels?

I want to train a CNN for a multilabel image classification task using keras. However I am not sure how to prepare my tranining data. More specifically, I am wondering if I need training images that ...
5
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2answers
3k views

SMOTE for multilabel classification

I have a dataset with 77 different labels. Each sample has one or more of these labels. I did some data analysis and found out that the dataset is highly imbalanced - there are a large number of ...
5
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2answers
3k views

How to visualize results/errors of multilabel classifiers?

For multiclass classification you would normally choose a confusion matrix to plot the error of predicted classes against the target classes. What is the best way to visualize errors of multilabel ...
5
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1answer
511 views

Solving multi label image classification using TimeDistributed dense layer

I have a multi label image dataset having 5 labels. Each image can have more than one label at the same time. I am using a convolutional neural network to extract features and those extracted features ...
4
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3answers
683 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 ...
4
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2answers
11k views

Class weight degrades Multi Label Classification Performance

I noticed something strange while I was conducting a multiple label classification problem via keras neural network. My data set consist of imbalance data with 12 features and 25 possible labels. When ...
4
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3answers
2k views

Categorizing Customer Emails

I am working on a project for a company which needs to categorize customer e-mails regarding loans and insurance. The e-mails are labeled uniquely from set of 13 category labels. The number of records ...
4
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1answer
37 views

Detecting off state in the magnitude of accelerometer data?

I have a univariate time series signal. It's the magnitude of an accelerometer attached to an engine. I need to create an algorithm to detect off state, please see the black lines in the image below....
4
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1answer
3k views

Keras decision threshold for Multiple Label prediction

I'm training a Neural Network to predict multiple labels for a given input. My input is a 200 sized vector of integers and the output should be a boolean vector of size 28. My ...
4
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1answer
11k views

What is the best way to deal with imbalanced data for XGBoost? [closed]

There are a lot of way to deal with class-imbalanced data like undersampling, oversampling, changing cost function etc. https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-...
4
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1answer
3k views

How to predict user next purchase items

I have an e-commerce website where customers can purchase items directly from the site. I have training data which includes order id, user id, order number, days since prior order, product id, add to ...
4
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2answers
203 views

Impact of unlabelled documents for label prediction via SVM

I have a corpus of text documents, some of which are labelled by analysts with label L. I am using this data to train an SVM for predicting if a new document should have label L. So far it's straight-...
4
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1answer
560 views

Multi-task learning for Multi-label classification?

I have a multi-label classification problem wherein each example can belong to one of the pre-defined classes (or can belong to none of them). I was wondering if I can somehow apply multi-task ...
4
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1answer
775 views

What does it mean that classes are mutually exlcusive but soft-labels are accepeted?

The Tensorflow's documentation of softmax_cross_entropy_with_logits: Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in ...
3
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3answers
20k views

Multi-class neural net always predicting 1 class after optimization

During training, the neural net settles into a place where it always predicts 1 of the 5 classes. My train and test sets are distributed as such: ...
3
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1answer
422 views

How to get feature importance from RandomForest using scikit-multilearn library?

I am working on multi-label classification problem, binary case. As a target variable there are five columns with 0-1 values. For a model training I use scikit-multilearn library. Below is my code ...
3
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1answer
986 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 ...
3
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2answers
6k views

Multi-class text classification with LSTM in Keras

I'm quite new to Deep Learning and trying to solve the problem of Multi-Class, multi-label text classification using Deep Learning. https://github.com/fchollet/keras/blob/master/examples/...
3
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2answers
2k views

How to deal with classification problem where labels are non uniformly distributed?

I have a data set which has around 1000 samples and are divided in 4 groups - A, B ,C , D. The problem I am facing is that there are very high number of data sample which have B and C s output. They ...
3
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1answer
1k views

Which Loss cross-entropy do I've to use?

I'm working with this dataset https://www.kaggle.com/c/sf-crime to predict the crime incident using keras. I've encoded the category with pd.get_dummies and then use it as the validation data. At ...
3
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1answer
557 views

Multilabel classifcation in sklearn with soft (fuzzy) labels

I have a model which is trained in sklearn on a 5-way classification problem, which performs relatively well (there are kNN and SVM versions, and both reproduce a test set with high accuracy). When ...
3
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1answer
93 views

How to utilize user feedback due to miss-classification when correct class label is unknown?

Suppose we are developing an app which is supposed to predict a dog's breed by it's picture. We trained a classifier (in my case an MLP) using some dataset and shipped the app to users. Now suppose ...
3
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1answer
74 views

Is there a way to cluster words based on how similarly they sound?

I have a list of words for a fictional world I've made (don't judge lol). My ultimate goal is to generate more words that sound like them through a markov generator, but for now, I'm trying to build ...
3
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1answer
171 views

Object Detection classification

I am currently training a classifier for detecting resistors using TensorFlow Object Detection API. For that, I downloaded resistor images from ImageNet and I am currently labeling those who will be ...
3
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1answer
2k views

Activation method and Loss function for multilabel multiclass classification

I am using CNN for Sentence Classification code by Yoonkim. This is used for text classification. I noticed that he uses softmax layer and negative log likelihood error. This is optimal for single ...
3
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1answer
104 views

Can reducing the number of classes in multi-label classification increase performance?

This is more of an open question with people which have experience in this. I'm working on a multi-class multi-label classification for chest x-rays. I would like to know how much can reducing the ...
3
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1answer
361 views

Classification algorithm with multiple output for a set of features

I want to build a classification algorithm that will predict multiple values for a set of features. For instance, lets say I have a customer demographic data like Income, age, sex, city and I want to ...
3
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1answer
233 views

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

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. ...
3
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1answer
1k views

Confusion matrix in multilabel classification of an object in more than one class simultaneously

Regarding a classification problem where for example given an image which depicts a human and we are trying to predict their stance and their behavior. For example Human 1: 'Sitting' and 'Eating' in ...
3
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1answer
5k views

How to correctly perform data sampling for train/test split in multi-label dataset?

Problem statement I have a text multi-label classification dataset, and I've found a problem with the dataset sampling. I'm facing two different strategies. The first one consists in preprocessing ...
3
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1answer
2k views

Large Numpy.Array for Multi-label Image Classification (CelebA Dataset)

Task: Build CNN Model (preferably Keras or TensorFlow) to Predict Labels Associated to Each Image in CelebA Dataset (Multi-label Image Classification) In past, for majority of multiclass/binary image ...
3
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1answer
2k views

Multi Label Classification on Data Columns in Tables

I am seeking guidance on a machine learning problem involving the tagging of data columns. Currently, I have a system where users can add multiple tags to a columns in a table. However, I want to ...
3
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3answers
1k views

What is a subspace and what is a shared subspace?

I'm totally new in machine learning. The first confusing concept is subspace. In multi label classification we have to share the subspace. What does one mean by that shared sub spaces?
3
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1answer
75 views

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 ...
3
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1answer
1k views

What is the better way to predict classes for the models developed using the functional API in Keras

We can predict the class for new data instances using the Sequential classification model in Keras using the predict_classes() function. What is the way to predict the class for models that developed ...
3
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1answer
161 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 ...
3
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0answers
1k views

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 ...
3
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0answers
281 views

How to add a new label to a multi-label dataset (like Open Images)

Given N classes in a multi-label dataset and a trained classifier C, how would we add a new class N+1 to the dataset, and fine-tune the trained classifier C such that it now predicts N+1 labels? (lets ...

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