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0
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1answer
19 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. ...
0
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0answers
11 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: ...
0
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0answers
26 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 ...
0
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0answers
54 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' ...
0
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0answers
18 views

What are “example based” or “label based” metrics?

In the context of multilabel learning I came across the notion of "label based" and "example based" metrics (see here) I'm familiar with precision, recall etc. but I'm not sure what those two ...
0
votes
1answer
64 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 ...
0
votes
1answer
34 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 ...
1
vote
1answer
378 views

scikit multi label classification

I am trying to classify data into four different labels. The training data looks something like: ...
2
votes
1answer
314 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 ...
1
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0answers
99 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
votes
1answer
143 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
votes
1answer
2k 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
31 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 ...
3
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2answers
2k 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 ...
7
votes
1answer
6k 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
55 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 ...
2
votes
0answers
88 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
86 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 ...
4
votes
2answers
927 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 ...
8
votes
1answer
1k 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 ...