Classifier chains

Classifier chains is a machine learning method for problem transformation in multi-label classification. It combines the computational efficiency of the Binary Relevance method while still being able to take the label dependencies into account for classification.

Problem transformation methods transform a multi-label classification problem in one or more single-label classification problems. In such a way existing single-label classification algorithms such as SVM and Naive Bayes can be used without modification.

Several problem transformation methods exist. One of them is Binary Relevance method (BR). Given a set of labels ${\displaystyle {\mathit {L}}\,}$ and a data set with instances of the form ${\displaystyle {\mathit {(x,Y)}}\,}$ where ${\displaystyle {\mathit {x}}\,}$ is a feature vector and ${\displaystyle Y\subseteq L}$ is a set of labels assigned to the instance. BR transforms the data set into ${\displaystyle \left\vert L\right\vert }$ data sets and learns ${\displaystyle \left\vert L\right\vert }$ binary classifiers ${\displaystyle H:X\rightarrow \{l,\neg l\}}$ for each label ${\displaystyle l\in L}$. During this process the information about dependencies between labels is not preserved. This can lead to a situation where a set of labels is assigned to an instance although these labels never co-occur together in the data set. Thus, information about label co-occurrence can help to assign correct label combinations. Loss of this information can in some cases lead to decrease of the classification performance.

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