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12 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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0answers
38 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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0answers
11 views

What is an “example based” metric?

In the context of multilabel learning I came across several "example based" metrics, for instance example based Recall, example based Precision etc. (see here) I do know the concept of Recall etc. ...
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0answers
46 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
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1answer
30 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
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1answer
328 views

scikit multi label classification

I am trying to classify data into four different labels. The training data looks something like: ...
2
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1answer
271 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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0answers
41 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 ...
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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
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1answer
132 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
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1answer
1k 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
30 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
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
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1answer
5k 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
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
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0answers
85 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
85 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
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2answers
870 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 ...