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1answer
27 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
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0answers
45 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
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0answers
45 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
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0answers
21 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
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0answers
25 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
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2answers
303 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 ...
4
votes
1answer
1k 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
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0answers
34 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
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0answers
57 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
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0answers
74 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
357 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
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0answers
530 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 ...