Multi-label classification where multiple target labels might be assigned to each instance.

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1answer
41 views

anomaly/outliers detection in a multilabel dataset on the outcomes

Assuming a multilabel dataset contains a few wrong data. If so, is there a way to predict those wrong outcome given the fact there is a 'pattern' in the predictors? Let's use 'baby and silly' ...
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7 views

Multi labels classification tools / algorithms that can support distributed computing

Currently I'm using mulan library, with random k labels method (in Java) for solving my multi labels classification problem. But I encountered a memory problem due to memory usage. My training file ...
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34 views

Training a CNN specifically for feature extraction

I am working on a multiclass multilabel image classification problem. I have been using pre-trained CNNs (from Caffe Model Zoo) to perform feature extraction. I then model the extracted feature ...
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2answers
19 views

Quantify quality of multi label assignment

I am interested in quantifying how well a multi label assignment performs. E.g. given 3 coloured boxes red, green and blue, with 20 likewise coloured balls in each. A monkey is handed all the balls ...
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0answers
20 views

How to create a realistic multi-label data set of web pages? [closed]

I am working on a project which is a web filter. The web filter is supposed to classify web pages using any multi-label classification technique and then block them if anyone of the category is found ...
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1answer
29 views

Is there a way in sklearn to do multi-label classification that takes into account inter-label correlations?

As far as I understand sklearn's OneVsRestClassifier creates one independent classifier per label, but I'd think one would lose the potential benefit of taking into account other label predictions ...
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54 views

Loss function for multiple predictions without ground truth

I'm searching for a loss function to determine the difference between two predictions made by several multi-label classifiers. Consider the following: Prediction of classifier A is [0.2,0.5,0.1,0.8] ...
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131 views

Multi label image classification using convolutional neural network in Python

I am working on multi label image classification problem. The dataset is given on this link. I am using Convolutional Neural Network (CNN) with fully connected neural network (NN) at the end. I am ...
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1answer
45 views

Is it possible to do random forest with multiple responses or combine such ensembles for multi-label classification?

I have a dataset which has numerous nominal responses, and many predictors. Each response is basically a pass/fail check of a certain test applied to the data. Multiple methods are applied to each ...
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1answer
18 views

Evaluating a clustering against multilabels

I have a clustering of text documents, where each document is uniquely assigned to a cluster. I have a set of labels (keywords) attached to each document. That is, each label may be applied to many ...
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18 views

Notation of marginal distribution for random vector variables

I am reading this article about multilabel classification. Unfortunately I am unable to understand the following notation, as found in section 2. Multilabel Classification: $P^{(i)}_x (Y_i) = ...
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42 views

How to compute average precision(AP) for multi label image classification?

In multi label image classification task like that in Pascal VOC 2012, AP and mAP are used to evaluate the performance. My question is: To compute the Average Precision, how many labels and the ...
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9 views

Empirically backed approach for using single label classifier on multi label problem?

My supervisor and I are looking into how we might adapt our proven model for using conditional random fields to label sentences in a document to generate multiple labels where appropriate. We are ...
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18 views

Question regarding K-Means Multilable problem

I have a dataset where for a set of features I have a single label but in my prediction I wanted to predict upto 5 labels for each test data. The labels are categorical and the number of distinct ...
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17 views

Do “bad training instances” decrease predictions quality in multi-label text classification with SGD?

I have 150k company descriptions (~140 characters long) tagged with approximately 1-6 industries. I have 110 possible industries. Industry distribution across different companies is not homogeneous: ...
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36 views

Grid Search for Multi-label Classification: Averaged or Individual Class(s) Score?

Apologize for unclear title, please help edit it to make clarity if possible. I am trying to use binary relevance to solve the multi-label problem. When it comes to model tuning, I am wondering ...
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1answer
34 views

how to make new class from the test data in machine learning

I have a list of accounts as data set and I need to group the accounts that refer to the same user using many features. I'm thinking to use machine learning( but I'm new in this domain), because I ...
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40 views

SVM vs. Ranking SVM?

I'm having a hard time visualizing Ranking SVM and would love help "drawing it out". Rank SVM is a multi-label multi-classification learning method, and Support Vector Machine was originally intended ...
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11 views

Correct implementation of binary relevance

I have questions regarding Binary Relevance implementation. How to treat multi-label problem with empty class? (I have instance that cannot have any label). Should I introduce new label "NoLabel" in ...
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39 views

Classifier puts everything into the same class, inspite of reasonable distribution?

First of all I'd like to describe the characteristics of the problem I'm working on, the things I've tried, and the problem I'm running into. I'm attempting to assess a client's propensity to pay for ...
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1answer
21 views

Herarchical class classifier with default parent class labelling

I have a hierarchy of classes for which I need to train a classifier which will assign the lowest level class in the hierarchy and default to an upper level class , is this possible to do with scikit ...
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0answers
14 views

Using a classifier's test error as a model evaluation metric for another model

I have a multi-class multi-label (MCML) dataset, built from a model I do not trust (assume I am right for distrusting it). Say I have 1000 patients with 14 feature scores, and 5 possible target labels ...
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33 views

Inter-annotator agreement in multi-label classification

I wonder what is the best metric to measure inter-annotator agreement in multi-label classification for two annotators?
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1answer
283 views

How to plot visualization for multi-label k-Nearest Neighbor?

I am studying multi-label learning methods, where for a given observation, you can assign more than one (a set of) target labels. One example is multi-label k-Nearest Neighbor. I am seeking a way to ...
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1answer
54 views

Rationale for Multi-Label vs. Single-Label learning?

I have not seen any research that compares the effects of single-label versus multi-label learning. What I mean by this is not comparing various types of evaluation metrics - such a comparison does ...
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61 views

Micro vs weighted F1 score

In a multi-label or multi-class classification setting, when choosing between a micro or a weighted F1 score, what shall I take into account? The main upside of choosing macro is that one gets a ...
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2answers
74 views

Multi-label classification

I am working on a project and I need some suggestions. I have a data set with 600 songs and for each song we have 60 numerical features (linked to the rhythm and the timbre of the sound). Moreover ...
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1answer
65 views

Bad results using Bayes Multinomial Navie in multi-label classification texts

I've been trying to classify multi-label texts with different classification algorithms. I get some pretty good results with linear kernel SVM and with the rest of the kernels the result is not good. ...
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16 views

reduced multilabel-dataset performance evaluation

Assume a multilabel problem with given ground truth, where each training instance can have one or more of 3 labels A,B and C, e.g: ...
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100 views

fuzzy clustering and multi-label classification

I’m working on a clustering problem that I would like to extend to multi-label classification. Basically, I want to generate a number (x) of clusters using something like fuzzy c-means and using the ...
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185 views

Confusion matrix with multi-class multi-label classification

Let's say I have three possible classes {'isCold' 'isWet' 'isSolid'} and my instances can belong to one or more of these classes. Ground Truth ice = {'isCold' 'isWet' 'isSolid'} water = {'isCold' ...
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2answers
309 views

best empirical macro/micro F1 score?

In the following presentation it's said that "0.5 to 0.55 (micro) F1 score is best for multilabel classification problems" I tried to investigate this statement but couldn't find the source. Does ...
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1answer
55 views

Suggestion for method/framework to use for short string classification with “complex” ouput

What I am trying to do : I have short text strings (max 128 total chars in length) which I would like to classify (or use for prediction) as belonging to a particular type of output (more on the ...
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1answer
2k views

scikit multi label classification

I am trying to classify data into four different labels. The training data looks something like: ...
5
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1answer
637 views

What is the difference between a multi-label and a multi-class classification?

What is the difference between multi-label classification and multiclass classfication. Speficially, what is the difference between a label and a class? Please provide a clear example. "Multiclass ...
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108 views

What should I use - Multi label classification or Multi class classification? [duplicate]

In my dataset, I have 2 labels, positive and negative. Most samples belong to only one class, either positive or negative. A small fraction of samples take both labels i.e. both positive and negative. ...
0
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1answer
238 views

Should I use multi-label classification?

I have a classification problem with 2 classes (positive and negative). Usually, in such classification problems, all the samples will be labelled either 'positive' or 'negative'. In my dataset, some ...
2
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1answer
3k views

“Mean average precision” (MAP) evaluation statistic - understanding good/bad/chance values

I'm evaluating a multilabel classifier. I'm familiar with the Area Under the Curve statistic, which has some nice properties (e.g. chance level is always 50%). But for some applications, it's more ...
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0answers
35 views

Are there any associative multi label classification implementations available?

I have seen that it is possible to perform multi label classification using a binary combination of classifiers or reducing a multi label classification to a multi class classification problem by ...
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2answers
4k views

How to apply neural networks on multi-label classification problems?

Description: Let the problem domain be document classification where there exists a set of feature vectors, each belonging to 1 or more classes. For example, a document ...
9
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2answers
9k views

How to use scikit-learn's cross validation functions on multi-label classifiers

I'm testing different classifiers on a data set where there are 5 classes and each instance can belong to one or more of these classes, so I'm using scikit-learn's multi-label classifiers, ...
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0answers
82 views

Single Multi-label classifier or multiple single-label classifiers?

I have a problem to classify my data that can fit into more than one class at same time. Based on an initial study, I came across "Multi-label classifier" that can classify data into more than one ...
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0answers
97 views

What is the method used in statistical classification when dealing with multi-dimensional discrete target labels?

If the training set is a set of $n$-tuples, with discrete labels, one can standardly use multinomial logistic regression (softmax), but what if the target labels are pairs of discrete values, or more ...
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0answers
94 views

Classifiers with post-training constraints on the prediction space

I'm familiar with using tools like SVMs and decision trees for discrete classification problems. But one detail that I have not encountered in that domain is: what do you do if your classifier must ...
5
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3answers
2k views

Multilabel logistic regression

Is there a way to use logistic regression to classify multi-labeled data? By multi-labeled, I mean data that can belong to multiple categories simultaneously. I would like to use this approach to ...
11
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1answer
2k views

Would a Random Forest with multiple outputs be possible/practical?

Random Forests (RFs) is a competitive data modeling/mining method. An RF model has one output -- the output/prediction variable. The naive approach to modeling multiple outputs with RFs would be to ...
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3answers
613 views

What are the measure for accuracy of multilabel data?

Consider a scenario where you are provided with KnownLabel Matrix and PredictedLabel matrix. I would like to measure the goodness of the PredictedLabel matrix against the KnownLabel Matrix. But the ...
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4answers
3k views

What is the difference between Multiclass and Multilabel Problem

What is the difference between a multiclass problem and a multilabel problem?