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15 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
10 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. ...
0
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0answers
35 views

what's the best empirical macro/micro F1 score?

Theoretically it should be 1. 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 ...
0
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1answer
27 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
251 views

scikit multi label classification

I am trying to classify data into four different labels. The training data looks something like: ...
2
votes
1answer
231 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
37 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
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0answers
98 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. ...
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0answers
60 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
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1answer
111 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 ...
0
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0answers
29 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
1k 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, ...
1
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0answers
53 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
82 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
83 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
815 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
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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 ...