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2
votes
1answer
75 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 ...
0
votes
0answers
7 views

maximize mean F1 score in multilabel information retrieval problem

I have a multilabel text classification problem where each observation will have one or more labels associated to it. The metric I want to maximize is mean F1 score. Are there standard ways to ...
1
vote
0answers
56 views

What should I use - Multi label classification or Multi class classification?

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
votes
0answers
35 views

What's the meaning of the class indicator matrix when transforming the class label matrix into it in canonical correlation analysis?

When using canonical correlation analysis (CCA), we can integrate the dataset and label information via transforming the class label matrix Y into the class indicator matrix T. Such as: $T = ...
0
votes
0answers
36 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 ...
1
vote
1answer
248 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 ...
0
votes
0answers
100 views

Dealing with class imbalance in multi-label classification

I have a set of around 300k text examples. As mentioned in the title, each example has at least one label, and there are only 100 possible unique labels. I've reduced this problem down to binary ...
0
votes
0answers
66 views

Accuracy vs F1 Measure in Multilabel Classification

I'd like to evaluate a multilabel classification algorithms and I was thinking of using both Accuracy and F1-Measure with: ...
0
votes
0answers
23 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 ...
0
votes
0answers
30 views

Is trying to get a set of probabilities of a document being assigned to different classes still a Multi-label classification problem?

I have a set of documents where a single document may belong to more than one class/category. However, what I am trying to produce when analysing a new document is not a set of the categories the ...
2
votes
2answers
530 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 ...
5
votes
1answer
2k 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, ...
1
vote
0answers
39 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 ...
1
vote
0answers
66 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 ...
4
votes
0answers
80 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 ...
3
votes
2answers
453 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 ...
7
votes
0answers
633 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 ...