Description Usage Arguments Value References Examples
Create a MLKNN classifier to predict multilabel data. It is a multilabel lazy learning, which is derived from the traditional Knearest neighbor (KNN) algorithm. For each unseen instance, its K nearest neighbors in the training set are identified and based on statistical information gained from the label sets of these neighboring instances, the maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance.
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mdata 
A mldr dataset used to train the binary models. 
k 
The number of neighbors. (Default: 
s 
Smoothing parameter controlling the strength of uniform prior. When
it is set to be 1, we have the Laplace smoothing. (Default: 
distance 
The name of method used to compute the distance. See

... 
Not used. 
cores 
Ignored because this method does not support multicore. 
seed 
Ignored because this method is deterministic. 
An object of class MLKNNmodel
containing the set of fitted
models, including:
A vector with the label names.
The prior probability of each label to occur.
The posterior probability of each label to occur given that k neighbors have it.
Zhang, M.L. L., & Zhou, Z.H. H. (2007). MLKNN: A lazy learning approach to multilabel learning. Pattern Recognition, 40(7), 20382048.
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Loading required package: mldr
Loading required package: parallel
Loading required package: ROCR
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